_PS 2306
POLICY RESEARCH WORKING PAPER   23 05
The Impact of Banking                                                     Policymakers in countries
undergoing banking crises
Crises on Money Demand                                                   should rot worry about the
and    Price  Stability                                   structural stability of money
demanci functions, the
behavior of money demand
Maria Soledad Martinez Peria                                              during crises can be modeled
by the same function used
during periods of tranquility
But policymakers should be
aware that in some instances
crises can give rise to variance
instability In the price or
inflation equations
The World Bank
Development Research Group
Finance                                                                            U
March 2000



| POLICY RESEARCH WORKING PAPER 2305
Summary findings
Martinez Peria empirically investigates the monetary               Overall, she finds no systematic evidence that ba.kieg
impact of banking crises in Chile, Colombia, Denmark,           crises cause money demand instability. Nor do trie resulLs
Japan, Kenya, Malaysia, and Uruguay. She uses                   consistently support the notion that the relationship
cointegration analysis and error correction modeling to         between monetary indicators aind prices undcr,oes
research:                                                       structural breaks during crises. Howevert altheolugi
* Whether money demand stability is threatened by            individual coefficients in price equations do nct seem, -o
banking crises.                                                 be severely affected by crises, crises can soeneti ines gim t
* Whether crises bring about structural breaks in the         rise to variance instability in price or inflation ;-qations.
relationship between monetary indicators and prices.
This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to study bankmng
crises. The study was funded by the Bank's Research Support Budget under the research project "Monetary Policy aind
Monetary Indicators during Banking Crises" (RPO 683-24). Copies of this paper are available free from the World Bank,
1818 H Street NW, Washington, DC 20433. Please contact AgnesYaptenco, room MC3-446, telephone 202 -473-i182
fax 202-522-1155, email address ayaptenco@worldbank.org. Policy Research Working Papers are also posted c n th  Web
atvwww.worldbank.org/research/workingpapers. The author may be contacted at mmartinezperia@ aworldbank.crg. vNilard :
2000. (81 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas ab,rs
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. T heI
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and con-clusions expressed In tbis
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors oi rA.17I
countries they represent.
Produced by the Policy Research Dissemination Center



The Impact of Banking Crises on Money Demand
and Price Stability
by
Maria Soledad Martinez Peria*
The World Bank
I am grateful for helpful discussions with Neil Ericsson, who consulted for the World Bank on this
project. I am indebted to Tomas Balifno, Jerry Caprio, Patrick Honohan, David Marston, and Sergio
Schmukler for very useful comments and suggestions. I would also like to thank Cristina Neagu and
Ivanna Vladkova for excellent research assistance. All numerical results were obtained using PcGive
Professional version 9.1; see Hendry and Doomik (1999). A similar version of this paper will be
published in the Intemational Monetary Fund Working Paper Series.
Address: 1818 H Street NW, Washington, DC 20433. Telephone: (202) 458-7341. Fax: (202) 522-2106.
E-mail address: mmartinezperia@worldbank.org.



I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



I - Introduction
Banking crises have plagued countries around the world from Argentina to Zamnbia over
the last two decades. In recent years, several papers have focused on identifying banking crisis
episodes and studying their causes.' However, until very recently, the importance of a sound
banking sector for monetary policy implementation did not receive much. attention. Two
exceptions are the recent studies by Garcia-Herrero (1997) and Lindgren, Gjarcia, and Saal
(1996). Both studies describe some of the distortions and problems that banking crises can create
for the assessment and implementation of monetary policy. They argue that banking crises
complicate the conduct of monetary policy because they destabilize money demnand and money
multipliers, they diminish the effectiveness of monetary instruments, and they affect the
relationship between monetary indicators and prices. Ultimately, banking crises, they argue, may
reduce the government's ability to achieve its inflation objective.
Monetary indicators refer to variables that help explain the behavior of prices and are
monitored by policy-makers to guide them in the conduct of monetary policy. Also, these
variables are typically included in the empirical equations for prices. Monetary aggregates are
frequently used as monetary indicators.2 Central banks monitor the behavior and demand for
monetary aggregates because they are reputed to be useful in explaining the behavior of prices.
Furthermore, these variables are readily available to the monetary authorities at high frequencies,
and they are considered to be better measured than other indicators.
1 See Caprio and Klingebiel (1996), Demirguc-Kunt and Detragiache (1997), and Lingren, Garcia, and
Saal (1996).
2 Monetary aggregates have been traditionally used as targets for the conduct of monetary policy because
they were thought to have a tightly controllable and reliable link to prices. Over time financial innovation
and other factors have led central banks to abandon the use of monetary aggregates as strict targets for the
conduct of monetary policy. Instead, monetary aggregates are increasingly being used as monetary
indicators.
2



Garcia-Herrero (1997) and Lindgren et al. (1996) argue that, because banking crises
destabilize the demand for money, they are likely to affect the relationship between prices and
monetary aggregates. Thus, they argue monetary authorities may benefit, in particular during
crises, from expanding the set of indicators they monitor to include other indicators like
exchange rates, interest rates, and stock prices. Though very informative, these studies rely
heavily on a descriptive approach rather than on a systematic econometric evaluation of the
problems that banking crises may bring.3
This paper conducts an empirical analysis of the monetary effects of banking crises. We
research two issues. First, we evaluate the claim that money demand stability is threatened by the
occurrence of banking crises. Secondly, we analyze the relationship between monetary indicators
and prices and, in particular, we test whether crises cause a structural break in this relationship.
The study focuses on the following country and crises episodes: Chile (1981-87),
Colombia (1982-1988), Denmark (1987-1992), Japan (1992-present), Kenya (1985-1989, 1992-
1995), Malaysia (1985-1988), and Uruguay (198 1-1985).4' These countries were chosen in order
to obtain a geographically representative sample of countries that experienced banking problems
over the last two decades.6
3 Garcia-Herrero (1997) conducts a Johansen-type cointegration analysis to study long-run money
demand stability, but she warns that her analysis is incomplete and that her sample is too short. Lindgren,
Garcia, and Saal (1996) cite evidence found by Balifno and Sudararajan (1991) that broad money demand
intercepts and interest elasticities change during banking crises in Argentina, Chile, Philippines, Spain,
and Uruguay. However, Balifio and Sudararajan's analysis does not contemplate issues like cointegration
and error correction modeling, so it is unclear whether the equations they base their results on are well
specified.
4 The dates in parentheses correspond to the periods identified by Caprio and Kinglebiel (1996) and
Lindgren, Garcia, and Saal (1996) as periods of banking crises.
5 Table A. 1 in the appendix contains information on the causes, extent, and consequences of the crises we
focus on.
6 Though we started our investigation with a sample of 17 countries that experienced crises over the last
two decades, data limitations reduced the number of countries included in the final analysis to the 7
mentioned above.
3



In our empirical estimations, we use cointegration analysis and error correction modeling
to find appropriate dynamic specifications for money and prices in each of the countries under
study. Parameter constancy tests on the estimated money demand equations help us evaluate the
hypothesis that money becomes unstable during periods of crisis. We focus on broad money
since the demand for narrow money is more likely to be affected by issues such as financial
innovation and deregulation, events that can themselves lead to instability. Finally, aside from
examining which variables are significant indicators of the behavior of prices, we also perform
parameter constarncy tests to determine whether crises bring about a structural break in the
relationship between prices and monetary indicators.
Overall, this paper does not find any systemic evidence that banking crises cause money
demand instability. Regarding the determinants of prices, we find that money, exchange rates,
foreign prices, and domestic interest rates are significant indicators of price behavior. Finally, the
results do not support the notion that the relationship between monetary indicators and prices
undergoes a structural break during these episodes. However, for three out of the seven countries
in this study, there is evidence of variance instability in the price equations as a result of banking
crises.
The rest of the paper is organized as follows. Section II briefly reviews the relevant
literature. Section III outlines the empirical methodology used in this paper. Section IV presents
the empirical results. Finally, section V concludes.
II - Literature Review
A number of papers have studied the demand for money and the determinants of inflation
in the countries included in this paper. Table A.2 in the appendix summarizes most of these
4



papers. These studies help guide the construction of the money demand and price/inflation
specifications. Wherever possible and appropriate, we try to use the same measures of the "own"
and "outside" rates of money for each country and to include most of the variables found to be
significant in previous studies.7 However, the majority of these papers cover different sample
than we do, and also they do not explicitly examine the impact of banking crises on the stability
of money demand.
The modeling and empirical approach used to estimate the demand for money in this
study resembles that of Baba, Hendry, and Starr (1992), Ericsson, Hendry, and Prestwich (1998),
and Ericsson and Sharma (1998). These papers focus on different countries and are not
concerned with the impact of banking crises on money demand. However, we follow these
papers in their treatment of issues like cointegration, error correction modeling, and parameter
constancy.
There is a vast empirical literature on the "information content" (i.e., ability to explain
prices) of monetary indicators that is related to the analysis conducted in this paper.8 Most of
these studies evaluate the information content of monetary indicators by estimating vector
autoregressive models (VARs) of prices, monetary aggregates, and other potential monetary
indicators and by conducting F-exclusion tests to determine the marginal explanatory power of
each indicator in explaining prices. This literature has mostly focused on the case of the U.S. and
other developed countries.9 Furthermore, to our knowledge, the existing literature has not
7 The "own" return on money (M2 in this paper) typically refers to the average rate on deposits included
in M2. The "outside" rate of money refers to the average rate on some alternative asset not included in
M2 (typically T-bills or government bonds).
8 See Baumgartner and Ramaswamy (1996), Baumgartner, Ramaswamy, Zettergren (1997), Caramazza
and Slawner (1991), Davis and Henry (1994), Friedman and Kuttner (1992), Hamann (1993), Hostland,
Poloz, and Storer (1987), Mahdavi and Zhou (1997), Sims (1980), Stock and Watson (1989), among
others.
9 Hannan (1993) is an exception. This study examines the relationship between money, output, and prices
5



empirically analyzed the impact of banking crises on the relationship between prices and
indicators.
The problem with the studies that focus primarily on the information content of monetary
indicators is that changes in their explanatory power may be caused by increases in their
volatility or noisiness over certain samples. Also, changes in the degrees of freedom in the
estimation of the price equation can also affect the results. For example, a preliminary analysis
we conducted indicates that the explanatory power of most monetary indicators, including
money, drops during crisis periods, relative to tranquil periods.10 However, the lack of statistical
significance of certain variables may very well be due to the loss of degrees of freedom over the
much shorter crisis periods.
This paper improves and adapts the methodology on the information content of monetary
indicators described above, in order to study the impact of banking crises on the relationship
between prices and indicators. Instead of focusing on examining the explanatory power of certain
variables over different samples, this paper tests for potential structural breaks in the relationship
between prices and monetary indicators. Structural stability is a more relevant matter for policy-
makers than the issue of whether a given variable happens to be statistically significant over a
particular sample. As long as the pre-crisis price equation remains stable over the crisis periods,
policy-makers can continue to use this formulation to model prices.
This study also pays substantial attention to the issue of cointegration (i.e., the potential
long-run relationship between prices and monetary indicators), which has been ignored by most
studies on the information content of monetary indicators. Finally, aside from modeling prices as
a function of domestic monetary and financial variables only (as most studies do), following De
in a group of Pacific Basin countries that underwent a process of financial liberalization during the 1 980s.
'° Results are available upon request.
6



Brouwer and Ericsson (1998) and Juselius (1992), we also control for the potential impact of
wages, unemployment, and external factors on prices.
III - Empirical Methodology and Data
To examine the monetary impact of banking crises, we estimate dynamic money demand
and price/inflation equations using monthly data for each country for the period 1975-1998."
The purpose of estimating these equations is twofold. First, we want to determine whether
money demand becomes unstable during banking crises. Secondly, we want to test whether
crises cause a structural break in the relationship between monetary indicators and prices.
A number of steps are involved in the empirical analysis and testing of the issues
discussed above. First, we conduct unit root tests to determine whether the variables included in
the empirical analysis are stationary (see section 111. 1). Second, we test for cointegration between
prices and the monetary, labor, and external factors determining prices (see section H11.2). Third,
we obtain single equation error correction models for money and prices (see section 111.3).
Finally, we conduct parameter constancy tests to examine the stability of the money demand and
price/inflation equations (section III.4).
III. I Testing the presence of unit roots
Standard inference procedures do not apply to regressions that contain non-stationary
series. Therefore, for each country, we conduct augmented Dickey-Fuller (1981) unit root tests to
evaluate whether the variables used in our empirical analysis are stationary.
Given a series
t The sample for individual countries might be smaller than 1975-1998 depending on data availability.
See the data appendix.
7



yt =   + fly, - I+ a    (1)
where t and ,B are parameters and £t is assumed to be white noise. Yt is stationary if -I < ,B<I. The
augmented Dickey-Fuller test to determine if y, is non-stationary is carried out by estimating an
equation with Yt-J subtracted from both sides of the equation and adding lagged difference terms
to control for higher order correlation in the series.
Ay, = , + (fl-1)yI - i +51Ay1 - I + ±62Ayt -, +... + 6p -lAyI - p   I + a(2)
This augmented specification is then used to test whether ,B-1=0 against the alternative that , -
1<0. Dickey and Fuller (1981) have determined the distribution and the critical values for this
test. Finally, non-stationary variables are differenced as many times as needed (depending on the
variables' order of integration) until stationary is achieved.
1I1.2 Testing cointegration
Following Juselius (1992), we model domestic prices in each of the countries in our
sample as a function of monetary, external, and cost push factors. In other words, we assume that
consumer price inflation can be associated with inflation in the labor markets, that is wages being
above the underlying steady-state level; with monetary inflation, that is, excess money, and with
imported inflation.
For each country, we conduct Johansen (1988) cointegration tests to determine whether
there exist any long-run equilibrium relationships in the monetary, labor, and external sectors.
Given a vector autoregressive system (VAR) of order p:
y, = Aiy. - I +... + Apy,- p + u,  (3)
8



where yt is a k-vector of non-stationary I(1) variables and ut is a vector of innovations. The VAR
can be re-written as:
P-i
Ay = Fy - i+   FiAy - j+ u,       (4)
i=1
where
p                    p
1I=YAi-I    ri=-Ai
1=1                 ,j=i+1
According to Granger's representation theorem, if the coefficient matrix [I has rank r<k,
then there exist kxr matrices a and ,B each with rank r such that H=afX' and ,B'yt is stationary. r
determines the number of cointegrating relations (the cointegrating rank). Each column of ,B is a
cointegrating vector. The elements of a are referred to as the adjustment parameters in the vector
error correction model.
Johansen's test of cointegration consists of estimating the HI matrix in an unrestricted
form and testing whether we can reject the restriction implied by the reduced rank of Hl. If there
are k endogenous variables, with one unit root each, there can be from zero to k-I linearly
independent, cointegrating relations. The trace and maximal-eigenvalue statistics are used to test
the number of cointegrating vectors.12
The distribution of the cointegration tests is affected by the assumptions made about the
deterministic parts of the model. In other words, the distribution of the test depends on whether
we allow for a trend and/or constant term (see Johansen and Juselius (1990), and Johansen
12 77r = -T_   log(1-Ai) is the trace statistic and r = -Tlog(i- Ar+I) refers to the maximal eigenvalue statistic.
In both cases, r=O, 1,2..k-2,k- 1.
9



(1994)). In this paper, the constant and seasonals enter unrestrictedly in the VAR. Also, we allow
for a linear trend in the cointegration space. 13
As in Juselius (1992), we conduct the cointegration analysis in each sector (monetary,
labor, and external) separately, rather than examining cointegration among all possible
determinants of inflation for a number of reasons. In the first place, the data sample is not large
enough to examine systems including as many as ten variables. Secondly, as indicated by
Juselius (1992. p406) "a drawback of the analysis of the multivariate cointegration model is that
the difficulties of interpreting the cointegration space grow when more variables are added to the
VAR system."
1112. a Cointegration testing in the monetary sector
We test for cointegration among the variables in the vector Z1t={m,p,y,lo,Ja,Ap,t} where
m is the logarithm of nominal or real M2 (depending on the order of integration of M2), y is the
logarithm of a measure of income (usually industrial production measured in logarithms), I is
the level of the own rate of return on M2 (in most cases an average deposit rate), Pa is the level of
a measure of the return on alternative assets outside from M2 (e.g., government bonds or bills),
and t is a time trend. In those cases where there is evidence that money is I(1), we exclude
inflation, Ap (defined as the change in the logarithm of prices), since this variable will be
stationary.
13 We include a trend in the cointegration space in order to obtain a test for cointeg,ration invariant to the
value of the constant term (see Johansen (1995)). Also, we restrict the trend to the cointegration space
since we typically do not think that growth rates are quadratic, which they could be if the trend entered
unrestrictedly.
10



11.2. b Cointegration testing in the labor/wage sector
If wages and prices are I(1), we test for cointegration between the variables in the vector
Z t={w,u,p,t}. In this case, w corresponds to the logarithim of nominal wages, u is the log of the
unemployment rate, and p is the log of prices. Once again, t is a time trend. For the countries
where prices and wages are 1(2), we obtain an I(1) representation by examining cointegration
among the following variables Z2bt={w-p ,u,Ap,t} where w-p is the real wage (defined as the log
of wages minus the log of prices) and Ap is, once again, the inflation rate.
III 2.c Cointegration testing in the external sector
If domestic and foreign prices are I(1), we test for cointegration among the vector of
variables Z3at'={p,e,p*,I,I*, t}. In this case, p corresponds to the logarithm of domestic prices, e
is the log of the exchange rate with respect to the dollar or deutsche mark depending on the
country, p* is the logarithm of the foreign price level (represented by the U.S. or German price
level depending on the country), I is the domestic interest rate, and I* is the corresponding
foreign (U.S. or German) interest rate.14 Following Juselius (1992), we include interest rates in
the cointegration analysis, because the determnination of exchange rates takes place in both the
goods and capital markets. Therefore, we need to account for the interaction between them to
understand the external effects on prices.
When there is evidence that domestic prices could be 1(2), we examine cointegration
among the following variables Z3bt ={p-e,p*,Ap, 1,1* }. Once again, Ap is the inflation rate.
14 For Chile, Colombia, Japan, Kenya, Malaysia, and Uruguay, the exchange rate used is that of each
country's domestic currency vis-a-vis the dollar. Also, for these countries the relevant foreign price level
is the U.S. price level, and the foreign interest rate is the rate on U.S. government t-bills. In the case of
Denmark, we use the krone/deutsche mark rate, German prices are the relevant foreign prices, and we use
the interest rate on German government bonds as the relevant foreign interest rate. The exchange rate is
expressed as domestic currency per unit of foreign currency.
11



1 3 Single equation error correction modeling
After testing for cointegration in the monetary sector, we develop an error correction
model (ECM) for rnoney for each country in our sample. The conditional ECM for money is of
the form:
k-I        k-I        k-I       k-i        k-I        k-I
Am, = c + E /1Aml, + E 72API-i + E y31Ay,, + E r40jt1, + E 75Xt_; + E Y6ZAe, + A, ECMmoney,l + w, (5)
i=1        i=O        i=O       i=O        i=O        1=o
where C°t is a white noise error term. ECMmoney refers to the cointegrating vectors found (if any)
for the monetary sector. The remaining variables have been defined above. For those countries
where there is evidence that money and prices are 1(2), Am is replaced by A(m-p) and Ap is
replaced by AAp, the second difference of prices..
Similarly, we develop an ECM to analyze the short-run and long-run determinants of
prices/inflation. This ECM model incorporates the cointegrating vectors found for the monetary
(ECMmoney), labor (ECMwages), and external sectors (ECMexternal;). The ECM for prices is of
the form:
k-I         k-I         k-          k-I          k-I         k-1
Api = C + }r1jAp, j + 7r2iAM,-, +   Z3Ay,_i + ZT4N1 +i r5i,l + E  6irwj +
i=-        i=l          i=O         i=O          i=O         i=O
k-I         k-I         k-I         k-I         k-I
Z  r71AU   +E  81Aei +   2riAp,i +      Z OiZQ  I, +E    P  +
i=o     ~i=O         i_=O        i=O         i=O
a* ECMmoney -, + a2 * ECMwages -, + a3 * ECMexternal, -, + vt  (6)
where vt is a white noise error term and the majority of the remaining variables are defined
above. Asp refers to the change in stock prices. In those cases where money, prices, and wages
are 1(2), the first differences of these variables (Amt, Apt and Awt) are replaced by their second
differences (AAmt, AApt, and AAwt respectively).
12



After estimating the ECM equations for money and prices, we reduce these models to
obtain parsimonious representations. In other words, we exclude all insignificant variables and
lags. At each stage, we conduct F-tests to compare the previous model with the latest reduced
version of the model, in order to verify that the restrictions implied by the reduced model are
indeed accepted.'5
1. 4 Testing parameter constancy
We examine the stability of the single equations for money and prices in a number of
ways. First, we perforn Hansen (1992) tests for individual coefficient, variance, and joint (error
variance and coefficients) stability. In general, these tests may have low power because the
break-point is unknown.16 Secondly, we present sequentially estimated one-period ahead and
break-point Chow (1960) statistics. Third, to test whether the instability arises explicitly from the
crisis period, we report a Chow-type F-test, which we label F-CRISIS. This test compares the
equations estimated over the whole sample (i.e., the sample including the crisis and tranquil
periods) with the estimates for the period excluding the banking crisis. Finally, we interact the
regressors in the price equation with a dummy that equals one during crisis periods (and zero
otherwise) and we test whether these interaction terms are significant. The purpose of these
regressions is to study whether the relationship between prices and individual monetary
indicators is disrupted by crises.
5 These tests are available upon request.
16 See Hansen (1992).
13



II.5 The data
Monthly data on monetary aggregates, financial variables (like exchange rates and
interest rates) come from national sources (e.g., central bank bulletins, ministry of finance
reports, etc.) and international sources (IMF and OECD databases). Wherever possible, we also
control for the role of wages, the unemployment rate, and external factors (like foreign prices
and interest rates) in explaining prices. These variables come from the same sources mentioned
above. For all countries, we try to cover the period closest to January 1975 - June 1998. A data
appendix, at the end of the paper, describes the data used, the corresponding sources, and the
relevant sample periods for each country in our study.
IV - Empirical results
IV.1. Unit root tests
Because this study includes a significant number of countries and variables, we do not
discuss the unit root test results in detail here. However, Table A.3 in the appendix presents the
augmented Dickey-Fuller (ADF) statistics, for each variable, in each country, in the sample.
Every ADF statistic is reported for the shortest lag length obtainable without dropping a lagged
difference significant at the 5% level.
For all countries, the hypothesis of a unit root cannot be rejected for any of the nominal
variables in levels. Interest rates, output, the unemployment rate, and the change in the exchange
rate appear to be unequivocally I(1) in most countries. Also, in general, prices, M2, and wages
seem to be I(1). However, for Chile, Denmark, Malaysia, and Uruguay there is some evidence
that these variables may be 1(2). In particular, for these countries, either the Dickey-Fuller tests
accept the hypothesis of a unit root at the chosen lag length (or at surrounding lags), or the
14



estimated coefficient on the lag of the variable is close to one for the chosen lag length (or for
lags surrounding it).
Given the unit root test results, we conduct the cointegration analysis assuming that all
variables (in levels or log levels) are I(1) first. For countries were the evidence is mixed, we also
try an 1(2) approach. By an I(2) approach, we mean that we transform the supposedly I(2)
variables to obtain an I(1) representation before conducting the cointegration analysis (see
Johansen 1995). For example, in the cointegration analysis for money, if money and prices are
1(2), an I(1) representation implies examining cointegration between m-p and Ap, along with
other I(1) variables (typically interest rates). For each country, we report the results from the
approach that yields the most sensible results, given economic theory.
1V2. Cointegration results
As discussed in the previous section, we use Johansen's (1988) procedure to conduct the
cointegration analysis for each sector, in each country. We determine the lag length of the system
used to perform the cointegration analysis by estimating a regular VAR (starting at 14 lags or 13
lags depending on whether variables are I(1) or 1(2)) and sequentially reducing the model until
the F-test for the last lag of all remaining variables reject further reduction.'7
Below, we discuss the cointegration results for all countries, by sector affecting prices.
First, we display the results for the monetary sector (section IV.2.a ). Secondly, we present the
results obtained for the labor sector (section IV.2.b). Finally, we report the results for the
external sector (section IV.2.c).
7 The results from these tests are available upon request.
15



IV.2.a. Cointegration results for the monetary sector
The cointegration results for money are shown in Table 1. This table indicates the rank of
the system chosen, the names given to the cointegrating vectors found, and thle coefficients for
the cointegrating vectors. We report the lag of the system chosen, the actual trace and maximum
eigenvalue statistics, and the corresponding critical values in Table A.4 in the appendix.
For Chile, I)enmark, Japan, Malaysia, and Uruguay, we pursue an I(2) approach. In other
words, because we found some evidence that money and prices are 1(2) in these countries, we
transform these variables to obtain an I(1) representation suitable to test for cointegration (see
Johansen 1995). Thus, we examine the cointegration between real money (mn-p), income (y),
inflation (Ap), the own rate of return on money (10), and its domestic alternative or outside return
(la). 8, 19
For all five countries mentioned above, we find at least one cointegrating vector that has
a long-run real money demand interpretation. We find that inflation always has a negative impact
on real money demand as expected, and income has a unit elasticity. The ownl rate of return on
money (i.e., the average deposit rate) is positive and significant in the equations for Denmark,
Japan, and Uruguay. Furthermore, the own and outside rates of return on money have opposite
and equal effects for Japan and Denmark.20,21 For Chile and Malaysia, interest rates do not affect
money demand.
18 Initially, given the unit root results, we assumed all variables to be I(l) and we tested for cointegration
between m,p,y, I', and la. However, for these countries this approach was unsuccessful (the results are
available upon request). Also, given that from the unit root tests there was some evidence that prices,
money, and wages are 1(2), we decided to test for cointegration using an 1(2) approach.
19 The exact definition of the return for money and the outside rate of return for each country is in the data
appendix.
20 Following Juselius (1998), we allow a dummy that captures the period after the withdrawal of capital
controls in Denmark to enter the cointegration space. This dummy is significant in the cointegration
vector for money demand, indicating that money demand fell following the banning of controls.
21 For Chile, Colombia, and Uruguay, we do not include the rate of return outside of money from the
16



Aside from a long-run relationship for real money, for Chile, Denmark, and Malaysia, we
also find evidence of the existence of other cointegrating vectors. For Chile, the second vector
indicates that income is stationary around a trend, while the third vector suggests that the deposit
rate is stationary. For Denmark, the Johansen procedure points to a rank 2 system. The second
cointegrating vector for Denmark reflects a positive relationship between inflation, output, and
interest rates spreads. For Malaysia, aside from the money vector, we also find that income, the
own rate of return on money, and the outside rate are each stationary around a trend.
For Colombia and Kenya, where the Dickey-Fuller tests indicated that prices and money
are I(1), we test for cointegration between nominal M2 (m), prices (p), the own rate of return on
money (I°), and its alternative return (Ia). We find that for these countries at least one
cointegrating vector can be interpreted as a long-run money demand equation. We can also
accept price homogeneity and unit income elasticities for these countries. For Colombia, interest
rates do not seem to enter the long-run equation, while in Kenya we find that the outside or
alternative rate of return on money has a negative impact on money demand.
For Colombia, we also find a second vector that specifies that the own return on money is
trend stationary. For Kenya, we find two extra vectors, aside from the money demand vector.
The second vector indicates a relationship between output, the outside interest rate, and a trend.
The final vector shows that the spread between the own and outside rates of return on money is
stationary around a trend.
Summarizing, the fact that we find evidence of cointegration in the monetary sector of all
countries, even though these countries underwent banking crises at some point in the sample,
indicates that the long-run stability of money demand is not threatened by these episodes.
cointegration equations since there was no consistent measure for these countries for the full sample
period.
17



IV. 2. b. Cointegration resultsfor the labor/wage sector
Table 2 reports the cointegration results for the labor/wage sector for all countries. In the
case of Chile, Denmark, Japan, and Uruguay, given that we found some evidence that prices and
wages are 1(2), we test for cointegration between real wages (w-p), inflaLtion (Ap), and the
unemployment rate (u).
For each of the four countries mentioned above, we find evidence of one cointegrating
vector with a long-run real wage interpretation. For Chile, Denmark, and Uruguay, we find that
inflation and the unemployment rate negatively affect real wages. In the case of Japan, aside
from testing cointegration between w-p, Ap, and u, we also include a dummy for July and June
1997 interacted by a trend. These variables aim to control for bonus payments paid in June
during the early part of the sample and later in July of each year. We find one cointegrating
vector where real wages are negatively affected by inflation. The unemployment rate does not
seem to enter this relation.
For Colombia, we test for cointegration between nominal wages, prices, and the
unemployment rate, given that we concluded from the unit root tests that prices and wages in
Colombia are I(1). We find that wages are positively affected by prices, but the unemployment
rate does not appear to play a significant role.
Finally, we do not report results for Kenya and Malaysia, because high frequency wage
data is not available for these countries, for the period under consideration.
IV 2. c. Cointegration results for the external sector
Table 3 presents the cointegration results for the external sector. For Chile, Denmark,
Japan, Malaysia, and Uruguay, where we found evidence that prices could be I(2), we use the
18



Johansen technique to test for cointegration between domestic prices expressed in foreign
currency (p-e), foreign prices (p*), inflation (Ap), the domestic interest rate (I), and the foreign
interest rate (I*).22 For Colombia and Kenya, we pursue an I(l) approach instead. Thus, we test
for cointegration between domestic prices (p), the dollar exchange rate (e), foreign prices (p*),
the domestic interest rate (I), and the foreign interest rate (1*). Foreign prices (p*) refer to U.S.
dollar prices in the case of Chile, Colombia, Japan, Kenya, Malaysia, and Uruguay. Also, for
these countries, the exchange rate is the domestic currency rate vis-a-vis the dollar and foreign
interest rates refer to the return on dollar assets (typically T-bills). For Denmark, foreign prices
are German prices, the foreign interest rate is the rate on deutsche mark denominated assets, and
the exchange rate is the krone/deutsche mark exchange rate.
For Chile, Denmark, Japan, Malaysia, and Uruguay, where the empirical evidence
indicates that prices are I(2), we find at least one cointegrating vector that has a purchasing
power parity (PPP) interpretation including a dynamic term, Ap.23 Furthermore, in the case of
Denmark, Japan, and Uruguay, there is evidence of a second vector that can be interpreted as an
uncovered interest rate parity (UIP) relationship, given that Ap=Ae.24
22 For Chile, Denmark, Japan, Malaysia, and Uruguay we test for cointegration between p-e, Ap, p*,i, and
i*, given that for these countries we found evidence that p is 1(2). In general, we find a cointegrating
vector between p-e, p* and Ap. This is evidence that p is 1(2). Also, this suggests that e or p* are 1(2). In
general, we do not think p* (German or U.S. prices) is 1(2). We tested for cointegration assuming p* to be
1(2), but the attempt was mostly unsuccessful. The only other possibility is that e is 1(2). The main
problem with this interpretation is that the Dickey-Fuller tests do not point to e being 1(2). A possible
explanation for this seemingly contradictory evidence is that Ae is I(t), but it has a large l(O) component
on top of it. Thus, when we look at it as a univariate process all we see is white noise. However, when it
comes to system analysis, it could be that p and e have matching 1(2) components that cancel out and that
may explain why we find cointegration between these variables.
23 In the case of Chile, we test for cointegration between p-e, e, Ap, and p*. We do not include interest
rates, because the money demand cointegration analysis suggested that the Chilean interest rate is
stationary.
24 If p and e are 1(2) and p-e is I(l), then Ap and Ae are each I(l), but Ap=Ae+I(O).
19



As mentioned above, for Colombia and Kenya, we exarnine cointegration between p, e,
p*, I, and I*, since p and e appear to be I(1) in these countries. For Colombia, we find evidence
of three cointegrating vectors. The first vector has a PPP interpretation. The last two vectors
indicate that I and I* are stationary. For Kenya, we find two cointegrating vectors. The first
vector is a combination of a PPP relationship and the I-I* spread. The second vector indicates
that the spread betwveen I and I* is trend stationary.
IV 3. Reduced single equation money demand results:
Is the stability of money demand affected by banking crises?
In this section, we present and discuss the results for the parsimonious, conditional,
single-equation model for broad money demand for each country in our sample. The main
purpose of this section is to test the constancy of broad money demand. In other words, we want
to test whether countries that have experienced banking crises are likely to exhibit non-constant
broad money demand functions.
Tables 4 to 1O report the estimated coefficients, standard errors, and test statistics for the
reduced and final money demand equations for Chile, Colombia, Denmark, Japan, Kenya,
Malaysia, and Uruguay, respectively. For all countries with the exception of Japan, we include
the change in the exchange rate (Ae) as a regressor in the single equation for money.25 We
introduce this variable to control for the possibility of flight to foreign currency in countries were
there are not a lot of competing assets relative to bank deposits, and/ or where the exchange rate
has been traditionally pegged to a foreign currency.26
25 With the exception of Denmark, where we use the krone/deutsche mark exchange rate, for all other
countries the exchange rate variable refers to the domestic currency rate with respect to the dollar.
26 We did not include the change in the exchange rate in the cointegration analysis, because we found this
variable to be I(O) for all countries according to the Dickey Fuller tests.
20



For Colombia and Kenya, where money and prices appear to be I(1), inflation is not
significant. We find that in all remaining countries, inflation, Ap (or the change in inflation (AAp
or A2p ) depending on the country), is significant and has a negative effect on the demand for real
money.
With the exception of Japan, changes in income (y) have a positive effect on the demand
for money. However, income is significant only in the equations for Chile and Malaysia.
The own rate of return on M2, I° (typically the average deposit rate), has a positive and
significant impact on the demand for broad money in Chile, Kenya, and Uruguay. This variable
is positive but insignificant for Colombia and Japan, and negative but insignificant in Denmark,
and Malaysia. Changes in the outside or alternative rate of return on money, la, have a significant
negative impact on money demand in Denmark and Kenya. However, this variable is
insignificant in the equations for Japan and Malaysia.
Exchange rate changes are mostly significant and have a negative impact on the demand
for broad money in Colombia, Denmark, and Kenya.27 In the case of Uruguay, the exchange rate
has both a positive and negative impact on broad money, depending on the lag length. However,
the overall effect is zero. The exchange rate is not significant in Chile and Malaysia. Finally, the
error correction terms associated with long-run money demand are significant and negative in the
dynamic money demand equations for all countries.
Tables 4 to 10, also present various diagnostic statistics, which show that the final
equations obtained are well specified. These diagnostics statistics are tests against various
alternative hypotheses: residual autocorrelation (AR), skewness and excess kurtosis (normality),
autoregressive conditional heteroskedasticity (ARCH), and heteroscedasticity (hetero). The null
27 An increase in the exchange rate represents a depreciation.
21



distribution of these statistics is designated by X2(.) or F(.,.), and the degrees of freedom are in
parentheses.
With the exception of the AR test for Japan, we can accept the null hypotheses for each
of these tests, in each of these countries. In other words, all equations for all countries are well
specified except for the fact that there is some evidence of residual autocorrelation for Japan.
- As mentioned above, parameter constancy is a critical issue we want to analyze
concerning the estimated money demand functions. Figures 4 to 10 show innovations, one step
residuals, sequentially estimated one-period ahead Chow (1960) and break-point Chow (1960)
statistics. In these figures, the sequentially estimated Chow statistics are labeled l upChows and
NdnChows, respectively.28 Also, Tables 4 to 10 report the Hansen (1992) coefficients, variance,
and joint test for parameter constancy. Finally, we test whether the model estimated over the
whole sample is equivalent to that estimated over the period excluding the banking crisis. This
test statistic is distributed as F(nl,n2), where nl is the number of observations in the crisis period
(i.e., the omitted observations), and n2 is the degrees of freedom of the model estimated over the
full sample. If this F-test -labeled F-CRISIS- rejects, then we can infer that the instability in the
money demand function arises from the period of the banking crisis, since the only difference
between the overall sample and the sample excluding the crisis is the crisis period itself.
According to the recursively estimated Chow tests, Hansen stability tests, and F-CRISIS
money demand in Chile, Denmark, Japan, and Malaysia appears to be stable' 29 So, from these
28 The recursively estimated Chow tests are only useful in those cases when they include the crisis
periods. In some countries, however, because our data sample starts well into the crisis, the recursive
estimates start after the crisis period or well into it. In these cases, we rely on the F-CRISIS and Hansen
tests for stability.
29 In the case of Chile and Japan, we observe some one-period ahead Chow statistics that reject at 5%, but
they are too few to jeopardize the overall stability of the estimated equation.
22



results, it seems that banking crises in these countries have not threatened the stability of the
money demand equations.
The Hansen tests as well as the one-period ahead and break-point Chow tests provide
evidence of parameter instability in the estimated money demand equation for Colombia.
However, the instability in the equation seems to be coming from the period after the banking
crisis. The Colombian banking crisis took place between 1982-87. When we estimate the model
through 1989, rather than the overall sample 1981-1998, we find no evidence of instability
according to the Hansen, and Chow tests (see figure 5.B.).
The one-period ahead Chow and, in particular, the break-point Chow tests provide some
evidence of money demand instability in Kenya. However, the evidence is very marginal at 5%
significance. Furthermore, the Hansen stability tests and the F-CRISIS indicate that the equation
is stable. So, overall, we believe the results for Kenya accept the hypothesis of money demand
stability.
Regarding the stability tests for the Uruguayan money equation, the results are mixed. On
the one hand, the Hansen tests accept stability, but the F-CRISIS test rejects. Given that the
Hansen tests typically have low power because the break-point is unknown, we are inclined to
rely more heavily on the F-CRISIS test results. The one-period ahead Chow and break-point
Chow tests are not particularly useful in this case because the recursive estimations conducted to
obtain these tests start after the crisis period. However, it is clear from these figures, in particular
from the residual bands, that the estimation in the 1980s was less precise and stable than during
the 1990s. This suggests that the banking crisis during the period 1981-85 may have affected
money demand stability in Uruguay.
23



To summarize, the results in this section show that with the exception of Uruguay, we
find no overwhelming evidence that banking crises jeopardize broad money demand stability. 30
Table 11 presents a summary of the stability test results for the money demand functions in all
countries. The evidence presented here, together with the cointegration results for money,
indicate that whatever changes may have occurred in the demand for money owing to the
banking crises can be explained by the same function used to model money demand at times of
tranquility. Thus, we find that banking crises do not systematically threaten the short-run or long-
run broad money demand functions.
IV 4. Reduced single equations for prices: Do banking crises cause structural breaks in
the relationship between prices and monetary indicators ?
Tables 13 through 19 report the coefficients, standard errors, and test statistics for the
parsimonious price single equations estimated over the full sample for each country. By full
sample, we refer to the period covering the crisis and the tranquil episodes.3' We find that lagged
changes in broad money (or the second difference depending on the country) have a positive and
significant impact on inflation (or its growth rate depending on the country) in Chile, Denmark,
Japan, Kenya, and Uruguay. Money is insignificant for Colombia and Malaysia.
For Denmark, Japan, and Uruguay, changes in income are positive and significant in
explaining prices. For all other countries, income is insignificant. Changes in the exchange rate
(dollar exchange rate with the exception of Denmark) are largely significant and have a positive
effect on inflation. Exchange rate changes are not significant for the Colombian and Danish price
equations.
30 In the case of Colombia, we found evidence of instability but it seemed to be arising in the 1990s, many
years after the financial crisis in this country.
24



Foreign price changes (U.S. for all countries except Denmark where we include German
prices) are, in general, positive and significant. On the other hand, foreign interest rates are
significant only in the cases of Colombia and Uruguay.
Domestic interest rates (typically, the rate of return on money and its outside rate) have a
negative significant impact on inflation. This is particularly the case for Chile, Japan, Malaysia,
and Uruguay. For Kenya and Denmark, the own rate of return on money has a positive and
significant effect.
Wage changes are significant for Colombia and Uruguay at 5% significance and for
Denmark and Japan at 10%. In general, increases in wages result in higher inflation. On the other
hand, increases in unemployment have a negative impact on inflation, but they are only
significant for the case of Chile and Uruguay. Finally, stock prices changes (denoted as sp) have.
no significant impact on consumer price inflation across country.32
The significance of the error correction terms varies largely across countries. The PPP
error correction terms are significant in the case of Chile and Denmark, while the error correction
term interpretable as a UIP relationship is significant in the price equations for Denmark, Japan,
and Uruguay. The money error correction terms affect prices in the equations for Denmark and
Japan. Finally, wage cointegrating vectors are significant only for Denmark and Japan.
At the bottom of Tables 13 to 19, we present the diagnostic tests for residual
autocorrelation (AR), skewness and excess kurtosis (normality), autoregressive conditional
heteroskedasticity (ARCH), and heteroscedasticity (hetero). None of the price equations reject
any of these specification tests. Thus, none of the estimated price equations present specification
problems.
31 For most countries, the full sample covers approximately the period 1975-1998.
32 Stock prices were only available for Chile, Colombia, and Japan.
25



We analyze the constancy of the estimated price equations using mostly the same
methodology discussed for the money demand equations (see section IV.3). Figures 13 to 19
show innovations, one step residuals, sequentially estimated one-period ahead Chow and break-
point Chow statistics. In these figures, the Chow statistics are labeled as lupChows and
NdnChows, respectively. Also, we report the Hansen coefficients, variance, and joint tests for
parameter constancy. In the Hansen tests, the break point date is unknown,, so a finding of
instability cannot be immediately connected to a given period. Therefore, to examine whether the
instability is arising directly from the banking crisis period, we conduct a Chow-type F-test
(labeled F-CRISIS as before) that compares the estimation of the model for the overall sample,
with the results obtained for the sample excluding the crisis period. We summarize the
information on these stability tests for the price equations in Table 12.
Both the Hansen tests, and the break point Chow test indicate that the Chilean price
equation is stable. Also, F-CRISIS fails to find any evidence that the banking crisis period led to
instability in the price equation. We obtain similar results for the Danish and Malaysian price
equations.
According to the sequentially estimated one-period ahead and break-point Chow tests, the
Colombian price equation appears stable. However, the Hansen tests reject stability. In
particular, these statistics point to variance instability. This seemingly contradictory results can
be reconciled by the fact that the recursive estimations start well into the sample. In other words,
the Chow tests are not very useful in this case, because they practically do not cover the crisis
period.33 The F-CRISIS test rejects the hypothesis that both periods can be explained by the same
33 The Colombian crisis took place between 1982-88. Recursive estimations for the price equation start
around 1987.
26



equation. This seems to point to the fact that the price equation is particularly unstable during the
banking crisis in Colombia.
Regarding the stability of the Japanese price equation, the Hansen tests indicate the
presence of instability. However, this applies only to variance instability and the evidence is
marginal, since the critical value for the Hansen variance test at 5% significance is roughly 0.5
and the test statistic is 0.52 (Hansen (1992)). Furthermore, the break-point Chow and F-CRISIS
tests, indicate parameter constancy over the crisis period.
In the case of Kenya, the Hansen tests for variance stability, the one-period ahead, and
the break point Chow tests indicate that the equation is not constant. In particular, we can
observe from the recursively estimated Chow tests that the instability seems to occur during the
1990s. Kenya experienced two banking crises one over the period 1985-89 and another over the
period 1992-95. The F-test for the 1980s crisis suggests that this period is not different from the
overall sample. However, the 1990s crisis does appear to be different than the non-crisis period.
The evidence on stability for the Uruguayan price equation is mixed. The Hansen test
accepts stability, but F-CRISIS, rejects. Thus, we are inclined to rely on the F-CRISIS result. The
one-period ahead Chow and break-point Chow tests are not useful in this case because the
recursive estimations conducted to obtain these tests start after the crisis period. However, we
can see from the residual bands that the estimation in the 1980s was less precise and stable than
during the 1990s.
The parameter constancy results discussed above focus mostly on the overall stability of
the price equations. In order to test whether individual coefficients in the price equation are
affected by the banking crises, we include interaction terms of each variable with a dummy that
takes a value of one during the crisis periods. These results are reported in tables 20 through 26.
27



With respect to the interaction terms for money, they are negative in the case of Chile,
Colombia, and Japan. This indicates that the coefficient on money is smaller during the banking
crises in these countries. However, Japan is the only country where these interaction terms are
significant. The i.nteraction terms for money are positive for Denmark, Kenya, Malaysia, and
Uruguay, but they are only significant at the 5% level in the case of the latter.
Regarding other indicators such as exchange rates, domestic interest rates, and stock
prices, we find only marginal changes in the coefficients for exchange rates during crisis periods
for Malaysia. Neither interest rates nor stock prices exhibit a significant increase or decrease in
their coefficients during the banking crisis periods. Furthermore, we find that with the exception
of Kenya, all interaction terms are jointly insignificant.
To summarize, the results from this section indicate that money, exchange rates, foreign
prices, and domestic interest rates are significant in explaining prices in most countries. Stock
prices, on the other hand, are not useful indicators of price behavior. In general, the relationship
between prices and individual monetary indicators is stable, despite the occurrence of crises.
However, in three out of seven countries we find some evidence of variance instability in the
price equations.
V - Conclusions
Until very recently, not much attention was devoted to the monetary impact of banking
crises. Two exceptions, Garcia-Herrero (1997) and Lindgren et al. (1996), warned about some of
the adverse effects of banking crises for the conduct of monetary policy. Using mostly a
descriptive approach, the authors argue that banking crises have significant implications for
money demand stability, for the effectiveness of instruments, for the relationship between prices
28



and monetary indicators, and for the overall impact of monetary policy. Though both of these
studies are very interesting and informative, they arrive at their conclusions without a systematic
empirical investigation of the issues they raise.
This study has attempted to fill this void in the literature on the monetary impact of
banking crises. Using cointegration analysis and error correction modeling, we examined the
claim that banking crises jeopardize money demand stability. Secondly, we used the same
empirical methodology to examine the overall stability of the process for inflation, as well as the
impact of crises on the coefficients of individual monetary indicators.
Our results suggest that the stability of money demand is not threatened by banking
crises. With the exception of Uruguay, we found that money demand functions are stable.
Regarding the indicators of price behavior, we found that changes in money, exchange rates,
foreign prices, and domestic interest rates seem to be useful in explaining prices. Finally, even
though in general we did not find that individual coefficients in the price equations change as a
result of banking crises, in three out of the seven countries, we uncovered evidence of variance
instability in these equations due to crises.
Given the results in this paper, we can draw two main conclusions that might be helpful
for policy-makers facing banking crises. First, policymakers in countries undergoing crises
should not be worried about the structural stability of money demand functions. Our results
indicate that the behavior of money demand during crises can be modeled by the same function
as during periods of tranquility. Second, although individual coefficients in the price equations
do not seem to be severely affected by crises, policy-makers should be aware that crises, in some
instances, can give rise to variance instability in the price/inflation equations.
29



References
Abdullah, Ahmad Zainudin and Zulkomain Yusop (1996), "Money Inflation and Causality: The
Case of Malaysia (1970-992)," Asian Economic Review, vol. 38, no. 1, 44-51.
Adam, Christopher (1992), "Recent Developments in Econometric Methods: an Application to
the Demand for Money in Kenya," African Economic Research Consortium, Special
Paper, No. 15.
(1992), "On the Dynamic Specification of Money Demand in Kenya,"
Journal of African Economies, vol. 1, no. 2, 233-270.
Apt, Jaime and Jorge A. Quiroz (1992), "Una Demanda por Dinero Mensual para Chile: 1983:1
1992:8," Revista de Analisis Economico, vol. 7, no. 2, 103-139.
Arize, Augustine C. (1990), "Effects of Financial Innovations on the Money Demand Function:
Evidence from Japan," International Economic Journal, vol. 4, no. 1, 59-70.
, S. L. Avard, V. Ukpolo (1997), "Multivariate Cointegration Tests of the
Impact of International Factors on Money Demand," The International Journal of
Finance, vol 9, no. 1, 482-504.
Baba, Yoshihisa, David F. Hendry, Ross M. Starr (1992), "The Demand for MI in the USA,
1960-1988," Review of Economic Studies, vol. 59, no. 198, 25-61.
Baumgartner, J. and R. Ramaswamy (1996), "Inflation Targeting in the United K<ingdom:
Information Content of Financial and Monetary Variables," IMF Working Paper,
WP/96/44.
and R. Ramaswamy, and Goran Zettergren (1997), "Monetary Policy and
Leading Indicators of Inflation in Sweden," IMF Working Paper, WP/97/34.
Bayoumi, Tamim (1998), "Japan: Selected Issues," IMF Staff Country Report No. 98/113.
30



Brownbridge, Martin (1998), "Financial Distress in Local Banks in Kenya, Nigeria, Uganda, and
Zambia: Causes and Implications for Regulatory Policy," Development Policy Review,
173-88.
Brunello, Giorgio (1995), "Recent Changes in the Internal Structure of Wages and
Unemployment in Japan," Journal of the Japanese and International Economies, vol. 9,
105-129.
Caprio, Gerard and Daniela Klingebiel (1996), "Bank Insolvency: Bad Luck, Bad Policy, or Bad
Banking?" World Bank -Annual Bank Conference on Development Economics.
Caramazza, Francesco and Slawner, Chad (1991), "The Relationship Between Money, Output
and Prices," Bank of Canada, Working Paper 91-4.
Chakrabarti, Santi K. and J. A. Ali (1992), "Prices in Kenya: An Empirical Investigation,"
Harvard Institute for International Development, Development Discussion Paper, 1-22.
Chow, Gregory (1960), "Tests of Equality between Sets of Coefficients in Two Linear
Regressions," Econometrica, 28, 3, 591-605.
Chye, Tan Eu, and M. Semudram (1988), "The monetary Versus Neo-Keynesian Controversy
over Inflation: The Malaysian Evidence," The Indian Economic Journal, vol. 36, No. 1,
July-September, 48-54.
Corker, Robert (1990), "Wealth, Financial Liberalization, and the Demand for Money in Japan,"
IMF Staff Papers, vol. 37, no. 2, June 1990, 418-432.
Cuthbertson, K, and M. P. Taylor (1988), "Alternative Scale Variables in the U.S. Demand
Function for Narrow Money: Some Non-nested Tests," The Cyprus Journal of
Economics, vol. 1, no. 2, 102-110.
Darby, M., Angel Mascaro and Michael Marlow (1989), "The Empirical Reliability of Monetary
31



Aggregates as Indicators: 1983-1987," Economic Inquiry, vol. XXVII.
Darrat, Ali F. (1985), "The Demand for Money in a Developing Economy: The Case of Kenya,"
World Development, 13, October/November, 1163-70.
Davis, E.P. and S.G.B. Henry (1994), "The Use of Financial Spreads as Indica,tor Variables:
Evidence for the UK and Germany," IMF Working Paper, WP/94/3 1.
Dhakal, Dharmendra, Magda Kandil, Subhash C. Sharma, and Paul B. Trescott (1993), "The
Inflationary Experiences of Six Developing Countries in Asia: an Investigation of
Underlying Determinants," Applied Economics, vol. 25, no. 3, 413-42 5.
De Brouwer, Gordon and Neil Ericsson (1998), "Modelling Inflation in Australia," Journal of
Business and Economics Statistics, Vol, No.4, 43 3-449.
Demirguc-Kunt, Asli and Enrica Detragiache (1997), "The Determinants of Banking Crises:
Evidence from Industrial and Developing Countries," The World Bank Policy Research
Working Paper 1828, September.
Dickey, D. and W.A. Fuller (1981), "Likelihood Ratio Statistics for Autoregressive Time Series
with a Unit Root," Econometrica, 49, 1057-1072.
Dominioni, Daniel and Jose-Antonio Licandro (1989), "La Banca Privada Uruguaya en la Crisis
de los Anos Ochenta," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol.
12, no. 2, 197-223.
Doornik, Jurgen and David F. Hendry (1997), Modeling Dynamic Systems Using PcFiml 9. 0,
International Thomsom Business Press, London.
Ericsson, Neil R. (1992), "Cointegration, Exogeneity, and Policy Analysis: An Overview,"
Journal of Policy Modeling 14(3), 251-280.
, David F. Hendry, and Kevin M. Prestwich (1998), "Tlhe Demand for
32



Broad Money in the United Kingdom, 1878-1993," Scandinavian Journal of Economics,
100(1), 289-324.
_ and Sunil Sharna (1998), "Broad Money Demand and Financial
Liberalization in Greece," Empirical Economics, 23, 417-436.
Fielding, David (1994) "Money Demand in Four African Countries," Journal of Economic
Studies, vol. 21, no. 2, 3-37.
Friedman, B. and Kenneth N. Kuttner (1992), "Money, Income, Prices and Interest Rates,"
American Economic Review, Vol. 82, No.3, 472-492.
Friedman, M. And Anna Jacobson Schwartz, A Monetary History of the United States, Princeton
University Press, Princeton, 1963.
Frowen, Stephen F. and Herbert Buscher (1990), "The Demand for Money in the U.S., UK,
Japan and West Germany: an Empirical Study of the Evidence since 1973," University
College London Dept. of Economics. Discussion Papers in Economics, No. 90-05.
Fullerton, Thomas M. Jr. (1993), "Inflationary Trends in Colombia," Journal of Policy
Modeling, vol. 15, no. 4, 463-468.
Garcia-Herrero, Alicia (1997), "Monetary Impact of a Banking Crisis and the Conduct of
Monetary Policy," IMF Working Paper,WP/97/124.
,"Banking Crises in Latin America in the 1990s: Lessons from
Argentina, Paraguay and Venezuela," IMF Working Paper, WP/97/140.
Geraghty, Coleen (1987), "Countrywatch: Malaysia, Banks Worry About Bad Debts and
Funding Costs," Asian Finance, 40-51.
Goldsbrough, D. and I. Zaidi (1989), "Monetary Policy in the Philippines during Periods of
Financial Crisis and Changes in Exchange Rate Regime: Targets, Instruments, and the
33



Stability of Money Demand," IMF Working Paper, WP/89/98.
Gosh, B. and S.C. Gan (1994), "On the Choice of Appropriate Money Stock for Malaysia," The
Indian Journal of Economics, nr. 297, 269-279.
Graziani, Carlo (1988), "La Dinamica de la Inflacion: el Caso de Uruguay," Monetaria, Centro
de Estudios Monetarios Latinoamericanos, Vol. 11, no. 1, 41-66.
Greene, William (1993), Econometric Analysis, Second Edition, Macmillan Publishing
Company, New York.
Habibulla, Muzafar Shab (1990), "Choice of Scale Variable and the Specification of the
Malaysian Money Demand function: A Note," The Indian Economic Journal, vol. 38, no.
1, 79-88.
_ (1991), "Money and Its Substitutes in a Developing Economy:
Empirical Evidence from Malaysia, " The Indian Economic Journal, vol. 39, no. 1, 60-
72.
Hamann, Javier (1993), "Financial Liberalization and the Information Content of Money in
Indonesia, Korea and the Philippines," IMF Working Paper, WP/93/88.
Hausmann, Ricardo and Liliana Rojas-Suarez, Banking Crises in Latin America, Inter-American
Development Bank, Washington D.C.
Hamilton, James (1994), Time Series Analysis, Princeton University Press, Princeton, New
Jersey.
Hansen, Bruce (1992), "Testing for Parameter Instability in Linear Models," Journal of Policy
Modeling, 1.4(4), 517-533.
Hendry, David F. and Jurgen A. Doornik (1999), Empirical Econometric Modeling Using
PcGive 9. 0, Timberlake Consultants Ltd., UK.
34



Herrera, Santiago and A., Juan Manuel Julio (1993), "La Demanda de Dinero en la Corto y en la
Largo Plazo en Colombia," Conyuntura Economica, vol. 23, no. 1, 91-107.
Herrera, Luis Oscar and Rodrigo Vergara (1992), "Estabilidad de la Demanda de Dinero,
Cointegration y Politica Monetaria," Cuadernos de Economia, vol. 29, no. 86, 35-54.
Horrigan, Brian (1986), "Monetary Indicators, Commodity Prices, and Inflation," Federal
Reserve Bank of Philadelphia, Working Paper No., 86-7.
Hostland, D., Poloz S., and P. Storer (1987), "An Analysis of the Information Content of
Alternative Monetary Aggregates," Bank of Canada Technical Reports. No. 48.
Hutchison, Michael (1998) "Empirical Determinants of Banking Crises: Japan's Experience in
International Perspective," University of California, Santa Cruz, Working Papers 422, 1-
36.
Johansen, S. (1988), "Statistical Analysis of Cointegration Vectors," Journal of Economic
Dynamics and Control, 12, 231-254.
and Katarina Juselius (1990), "Maximum Likelihood Estimation and
Inference on Cointegration - with Applications to the Demand for Money," Oxford
Bulletin of Economics and Statistics, 52, 2, 169-211.
(1991), "Estimation and Hypothesis Testing of Cointegration Vectors in
Gaussian Vector Autoregressive Models," Econometrica, 59, 6, 1551-1580.
(1994), "The Role of the Constant and Linear Terms in Cointegration
Analysis of Nonstationary Variables," Econometric Reviews, 13(2), 205-229.
(1995), Likelihood-based Inference in Cointegrated vector Autoregressive
Models, Oxford, Oxford University Press.
Juselius, Katarina (1988), "Cointegration and Identification in a Vector Time Series Model an
35



Application to the Demand for Money in Denmark," Discussion Papers from Institute of
Economics, University of Copenhagen, 88-03.
_ (1989), "Long-run Relations in a Well-defined Statistical Model for the Data
Generating Process. Cointegration Analysis of the PPP and the UIP Relations for
Denmark and Germany," Discussion Papers from Institute of Economics, University of
Copenhagen, 90-11.
.(1992), "Domestic and Foreign Effects on Prices in an Open Economy: The
Case of Denmark," Journal of Policy Modeling, vol. 14, no. 4, 1992, 401-428.
.(1993), "VAR Modeling and Haavelmo's Probability Approach to
Macroeconomic Modeling," Empirical Economics, 18, 1993, 595-622.
(1998), "Changing Monetary Transmission mechanisms within the EU,"
Empirical Economics, 23, 455-481.
Kamas, Linda (1995), "Monetary Policy and Inflation under the Crawling Peg: Some Evidence
from VARs for Colombia," Journal of Development Economics, vol. 46, 145-161.
Karatzas G. (1993), "Government Deficits, Monetization, International Reserve- Flows, and
Inflation in Selected African Countries," Economia Internazionale, 46, May-Aug, 201
-224.
Koskenyla, Heikki (1994), "The Nordic Banking Crises," Bulletin, Bank of Firlland, 15-22.
Lindgren, C., Gillian Garcia and Matthew I. Saal (1996), Bank Soundness and Macroeconomic
Policy, IMF publication services, Washington, D.C.
Machua, Wilfred (1986), "Kenya's Bank Failures: Bankers Speak Out," African Business,
22-24.
Magendzo W., Igal, "La Politica Monetaria en Chile en la Decada de los Noventa: Objectivos,
36



Herramientas e Indicadores," Serie de Estudios Economicos, No. 41.
Martner, Ricardo F. and Daniel Titelman K. (1993), "Un Analisis de Cointegracion de las
Funciones de Demanda de Dinero: el Caso de Chile," EL Trimestre Economico, Mexico,
vol. LX(2), 413-446.
Mathieson, D. and Haas, R. (1994), "Establishing Monetary Control in Financial Systems with
Insolvent Institutions," IMF Paper on Policy Analysis and Assessment, PPAA/94/10.
Matte, Ricardo E. and Patricio Rojas R. (1989), "Evolucion Reciente del Mercado
Monetario y una Estimacion de la Demanda por Dinero en Chile," Cuadernos de
Economia, vol. 26, no. 78, 195-216.
Mbitiru, Chege (1986), "Bank Collapse Forces Government Action," Africa Now, 31-32.
McNown, Robert and Myles S. Wallace (1989), "National Price Levels, Purchasing Power
Parity, and Cointegration: A Test of Four High Inflation Economies," Journal of
International Money and Finance, vol. 8, no. 4, 533-545.
Miller, Stephen M. (1991), "Monetary Dynamics: An Application of Cointegration and Error
Correction Modeling," Journal of Money, Credit and Banking, vol. 23, no. 1, 139-154.
Muhleisen, Martin (1995), "Monetary Policy and Inflation Indicators for Finland," IMF Working
Paper, WP/95/115.
Mwega, F. M. and Tony Killick (1990), "Monetary Policy in Kenya, 1967-88," Eastern Africa
Economic Review, vol. 6, no. 2, 117-142.
Njuguna Ndung'u (1993), "Dynamics of the Inflationary Process in Kenya," Ekonomiska studier
utgivna av Nationalekonomiska institutionen handelshogskolan vid Goteborgs universitet
,47.
Perez-Campanero, Juan and Alfredo M. Leone (1991), "Liberalization and Financial Crisis in
37



Uruguay (1974-1987)," IMF Working Paper,WP/91/30.
Reinhart, Carmen and Vincent R. Reinhart (1991), "Output Fluctuations and Monetary Shocks:
Evidence from Colombia," IMF Working Paper, WP/91/35.
Sheng, Andrew (1996), Bank Restructuring, Lessons from the 1980s, The World Bank, 1996
Sims, Christopher A (1972), "Money, Income, and Causality," American Economic Review,
62, 540-52.
________ , "Comparison of Interwar and Postwar Business Cycles: Monetarism
Reconsidered," American Economic Review, (Papers and Proceedings), 70, 250-7.
Soejima, Yutaka (1996), "The Long-Run Relationship between Real GDP, Money Supply, and
Price Level: Reexamination of Cointegration Test," IMES Discussion Paper 96-E-9.
Stock, James and Watson, Mark W. (1989), "Interpreting the Evidence on Money-Income
Causality," Journal of Econometrics, 40, 161-82.
Sundararajan, V. and T. Balifio, Banking Crises: Cases and Issues, IMF Publication Services,
Washington, D.C., 1991.
Tamura, Tatsuya (1992), "Monetary Control in Japan, " in Monetary Policy ain Financial
Innovations in Five Industrial Countries, Edited by Stephen F. Frowen, St. Martin 's
Press, New York, 101-119.
Wonsewer, Israel (1986), "La Inflacion en la Uruguay," Pensamiento Iberoamericano, Enero/
Junio, 167-172.
Wunder, Dieter (1990), "Inflacion en Colombia: Estimacion de un Modelo de Ecuaciones
Simultaneas," Estudios de Economia, vol 17. No. 1, Jun. 90, 283-342.
Yoshida, Tomoo (1990), "On the Stability of Japanese Money Demand Function: Estimation
Results Using the Error Correction Model," Bank of Japan Monetary aznd Economic
38



Studies, vol. 8, no. 1, 1-48.
Yoshikawa, Hiroshi and Yoshiyuki Takeuchi (1989), "Real Wages and the Japanese Economy,"
Bank of Japan Monetary and Economic Studies, vol. 7, no. 1, 1-39.
Yusoff, Mohammed bin (1988), "Money, Prices, and Balance of Payments in Malaysia," The
Indian Economic Journal, vol. 36, No. 1, July-September, 33-47.
39



TABLE 1 - Cointegration Results: Money Demand
Countryl          Rank   Cointegrating Relations
Vector Name                (standard error in parentheses)
Chile'               3
ChiECMrM2                   (m-p) -0.0308*Ap + y +0.0035*t
(0.0028)      (0.0005)
ChiECMytrend               y=0.0039*t
(0.0003)
ChiECMIown                  1°
Colombia             2
CoIECMnM2                   m=p + y + 0.0018*t
(0.0002)
CoIECMlown                  Io=-0.0308*t
(0.0089)
Denmark2             2
DenECMrM2                   (m..p) =-0.0207*Ap + y + 0.064*1o- 0.064*1A- 0.19*CapConDum
(0.0029)                (0.007) (0.041)
DenECMinfl                  Ap= 0.007*y + 0.002*10 - 0.0009*1A - 0.00002*t -0.003*CapConDum
(0.002)  (0.0002)   (0.0002)   (0.0000)  (0.0009)
Japan3               1
JapECMrM2                   (m-p) = -0.0909*Ap + y + 0.239*I° - 0.23*IA
(0.0085)                (0.049)
Kenya4               3
KenECMnM2                   m = p + y + -0.235*1A + 0.0174*t
(0.006)   (0.003)
KenECMgdp                   y= 0.065*10+ 0.0006*t
(0.005)   (0.0005)
KenECMispr                  10= IA - 0.0403*t
(0.009)
Malaysia5            4
MysECMrM2                   (m-p) = -0.25*Ap + y
MysECMytrend                y= 0.008*t
(0.0001)
MysECMIown                  Io== -0.0368*t
(0.0085)
MysECMlalt                  IA--0.038*t
(0.0083)
Uruguay6             i
UruECMrM2                   (m-p) = - 0.0172*Ap + y + 0.018*10 + 0.0027*t
I______           (0.0028)        (0.003)   (0.0006)
This table reports the restricted cointegrating relations found for the monetary sector. Johansen's (1988) methodlology
was used to determine the number of cointegrating vectors and to test parameter restrictions on these vectors.
Definition of variables: m represents the log of M2, p is the log of CPI, y is the log of a measure of income
(usually industrial production), 1 is the own rate of money, P4 is the alternative (outside) rate, and t is a time trend.
' Dummies for Dec.79, Jun.82, July82, Sept.84 and Oct.84 were included as unrestricted variables in the cointegration. See Table A.S.
2 The following dummies were included as unrestricted variables: DVAT, DPRSTOP, DCOTAX, D92Q4, D83Q1, D83Q2, Dec.92,
Jun.93, Jul.93, Aug.93. See Table A.S.
'The following dummies were included as unrestricted variables: May90, Apr.90, Apr.89, and Apr.97. See Table A.5.
4 The following dummies were included as unrestricted variables: Feb.80, Jul.81, Mar.88, Mar.93. See Table A.S.
'The following dummies were included as unrestricted variables: Jan.84, Feb.84, Mar.84. See Table A.5.
6 The following dummies were included as unrestricted variables: Nov.82, Dec.82, Dec.87, Jan.88, Nov.89, Dec.89, Jan.90, and Dec.92.
See Table A.5.



TABLE 2 - Cointegration Results: Wages
Country! Vector Name   Rank   Cointegrating Relations (standard error in parentheses)
Chile'                      I
ChiECMrwage                       (w-p) -0.0104*Ap - 0.2279*u - 0.0016*t
(0.0018)     (0.0428)  (0.0004)
Colombia"                   I
ColECMnwage                       w = p + 0.0012*t
(0.0001)
Denmark9
DenECMrwage                       (w-p) =-0.0424*Ap - 1.26*u + 0.0027*t
(0.0242)    (0.293) (0.001)
Japanl0
JapECMrwage                       (w-p) =-0.0294*Ap + 0.0066*DJuneT - 0.0066*DJulyT + 0.0008*t
(0.0085)     (0.0008)                           (0.0002)
Uruguay"'                   1
UruECMrwage                       (w-p) = -0.0025*Ap - 0.3483*u
________________   (0.0006)    (0.0486)
This table reports the restricted cointegrating relations found for the labor sector. Johansen's (1988) methodology
was used to determine the number of cointegrating vectors and to test parameter restrictions on these vectors.
Definition of Variables: w is the log of wages, p represents the log of CPI, u is the log of the unemployment rate,
and t is a time trend.
7 The following dummies were included in the cointegration analysis as unrestricted variables: Jun.82, Jul.82, Sep.84, and Oct.84. See Table A.5.
s The seasonal dummies included in this cointegration are centered seasonals. In addition, each centered seasonal is interacted with a dummy
variable for 1990. See Table A.5.
9 The following dummies were included as unrestricted variables: DumVAT, DumPRSTOP, and DumCOTAX. See Table A.5.
10 The following two dummies enter as unrestricted variables: Apr.89 and Apr.97. See Table A.5.
" The following dummies were included as unrestricted variables: Nov.82, Dec.82,Dec.87, Jan.88, Nov.89, Dec.89,
Jan.90, and Dec.92. See Table A.5.



TABLE 3 - Cointegration Results: External Sector
Country!          Rank   Cointegrating Relations
Vector Name               (standard error in parentheses)
Chile'             1I
ChiECMrPPP                (p-e) = -0.0506*Ap + p
(0.0083)
Colombia            3
ColECMnPPP                p = e + p
ColECMIdom
ColECMIfor                 *
Denmark'3           2
DenECMrPPP                (p-e) = -0.0097*Ap + p
(0.0022)
DenECMuip                 I = I + Ap - 0.015*t
(0.006)
Japan14             2
JapECMrPPP                (p-e) = -0. 1644*Ap + p* + 0.0046*t
(0.0178)          (0.0005)
JapECMuip                 r = I + Ap + 0.0237*t
(0.005)
Kenya               2
KenECMnPPP                p = e + p* + 4.016*1 - 4.016*1* - 0.337*t
(0.013)   (0.062)
KenECMidiff               I = 1.05*1 + 0.084*t
(0.016)
Malaysia'5          I
MysECMrPPP                (p-e) = 0.2696*Ap +p-
(0.0529)
Uruguay             2
UruECMrPPP                (p-e) = 0.0074*Ap + p* + 0.0058*t
(0.0026)         (3.1293)
UruECMuip                 1= Ap + I* -0.0487*t
(0.0181)
This table reports the restricted cointegrating relations found for the external sector. Johansen's (1988) methodology
was used to determine the number of cointegrating vectors and to test parameter restrictions on these vectors.
Definition of Variables: p represents the log of CPI, e is the log of the exchange rate (usually expressed as units
of national currency per US$), I is the domestic interest rate, I* represents the foreign interest rate, p * represents
the log of the foreign price level (CPI), and t is a time trend.
12 The following dummies were included in the cointegration analysis as unrestricted variables: Jun.82, Jul.82, Sep.84, and Oct.84. See Table
A.5.
13 A dummy for capital controls (dumcapcon) was allowed to enter as a restricted variable in the cointegration space. The following dummies
were included unrestricted: DVAT, DPRSTOP, DCOTAX, D924, 83QI, 83Q2, Dec.92, Jun.93, Jul.93, and Aug.93. See Table A.5.
4 The following two dummies were included as unrestricted variables: Apr.89 and Apr.97. See Table A.5.
s The following dummies were included as unrestricted variables: Jan.84, Feb.84, Mar.84. See Table A.5.



TABLE 4 - CHILE: Single Equation for Money (Modeling A(m -
Variable        Coff icient        Std. Error         t-value       Hansen Instabilit
Constant               -0.069            0.103             -0.676             0.12
A(m - p)t_ I           -0.130            0.069             -1.897             0.41
A2pA                  -1.002             0.244            -4.103              0.08
Ay,                    0.085             0.041             2.089              0.04
A0                     0.001             0.000             3.687              0.11
Ae,                    0.031             0.098             0.319              0.05
chiECMrM2,1            -0.050            0.009             -5.789             0.13
chiECMytrend,-.        0.085             0.024             3.593              0.12
chiECMIownt-1          0.007             0.000             3.697              0.15
Sample: 1978:10-1993:11
R2 =0.593958     F(24,157) =9.5691 [0.00001   c=0.0231
AR 1- 7 F( 7,150)     =  0.36271 [0.9226]
ARCH 7 F( 7,143)      =  0.3335 [0.9376]
Normality Chi2(2)     =   1.0704 [0.5855]
HETERO F(32,124)    =  0.57818 [0.9629]
Hansen Instability Test Results: Variance: 0.163305 Joint (variance & coefficients): 3.99966
F-CRISIS (80, 77) = 1.3427 [0.0975]
FIGURE 4 - CHILE: Recursive estimation for money demand
.05                                      .0
0~~~~~~~~~~~~~~~                      v M    i
.0S  _ _  _  _  _  _  _  _  _  _  _   _     L                             -1.-..
1985          1990                       1985           1990
Cf        I      I                 I      i    \ o-&4I 
2                            ~~~~~~~~~.75 
.25 
0~~~~I  ,                    pt  .25
0                          
1985          1990                       1985           1990
'For a list of and explanation for the dummies used in estimation, see Table A.5.



TABLE 5 - COLOMBIA: Single Equation for Money (Modeling Am )_
Variable        Coefficient        Std. Error         t-value      Hansen Instabiiy
Constant               0.334             0.070             4.743              0.16
Am, - 1                0.139             0.071             1.952              0.70*
AP, _ 6               -0.274             0.152            -1.806              0.09
Ay,                    0.032             0.023             1.356              0.35
AIO                    0.001             0.001             1.366              0.26
Ae, - 3                0.121             0.065             1.868              0.54*
Ae, 4                 -0.184             0.066            -2.765              0.54*
Aet -12               -0.136             0.066            -2.066              0.25
colECMnM2,1           -0.043             0.011            -3.891              0.17
colECMIown,-1         -0.000             0.000             -0.529             0.15
Sample: 1982:3-1998:6
R =0.584623           F(20,175)  12.315 [0.0000]    a=0.0 1223
AR l- 7 F( 7,168)     =  0.98872 [0.4412]
ARCH 7 F( 7,161)      =  0.88705 [0.5182]
Normality Chi2(2)     =  5.9558 [0.0509]
HETERO F(29,145)    =   1.4148 [0.09481
Hansen Instability Test Results: Variance: 0.713604* Joint (variance & coefficients): 4.75832
F-CRISIS (70,105) = 0.57249 [0.9933]
FIGURE 5a - COLOMBIA: Recursive estimation for money demand
E|--- nuovst                            [l. ResiStep  !....
1985         1990          1995           1985          1990         1995
5%  ---Ius CHO s|                    L1    5%a, Ndn4C OWs|
4~~~~~~~~~~~~~~
1i,1,!5j-;A<                                   V  ',
2
3-
1.5
2-
1985         1990          1995           1985          1990         1995
' For a list of and explanation for the dummies used in the estimation, see Table A.5.
* Denotes significance at 5%.



COLOMBIA: Single Equation for Money (Modeling Am), continued
For Sub-sample: 1982 (3) to 1989 (12)
Hansen Instability Test Results: Variance: 0.0860424 Joint (variance & coefficients): 4.29042
FIGURE 5b - COLOMBIA: Recursive estimation for money demand
|--- Innovs          |                             s-]     -     -Rot w  . -~       -___
-.020''                        I j      I '
'01~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I 
.01 -~~~~~~~~~~~¶
1985   1986   1987   1988    1989   1990        1985                                1990
5%  lu CHOWs         j           1 ~-~-~%-   Nd C1 i ii
'5L       0                        I          .75
27   '                                                      'A'-' I' '    \'  \ ' :\ Ii,
1985   1986   1987   1988    1989   1990    1985   1986   1987   1988    1989   1990



TABLE 6 - DENMARK: Single Equation for Money (Modeling A(m - pW
Variable         Coeffcient          Std Error            t-value       Hansen InstalijJ
Constant                 0.561              0.181               3.107               0.06
A(m - p)                -0.410              0.062              -6.611              0.07
A(m- p)                  10.178             0.056              3.193               0.11
A(. - p)                10.385              0.062              6.228               0.21
A(m- p)                  0.199              0.065              3.053               0.04
A2 p                    -.0.685             0.198              -3.456               0.1
2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A2p_ 5                  -0.288              0.160              -1.809              0.24
Ay,1                     0.024              0.030              0.809               0.43
AI0               =     .0004               0.003              -1.262              0.06
AA                      -0.005              0.002              -2.984              0.1 1
Ae,                      0.301              0.133              -2.262              0.05
denECMrM2,-1            -0.029              0.010              -2.842               0.06
denECMinflt-l           .0.002              0.006              -0.345               0.06
Sample: 1977:3-1993:12
R2 =0.886042            F(33,168)  39.583 10.0000]      as-0.0116
AR 1- 7 F( 7,161)           1.4047 [0.2068]
ARCH 7 F( 7,154)             1.383  [0.2162]
Normality Chi2(2)           3.5022 [0.1736]
HETERO  F(46,121)          0.70941 [0.9070]
Hansen Instability Test Results: Variance: 0.101549 Joint (variance & coefficients): 4.23773
F-CRISIS (68,100) =   1.1544 [0.2544]
FIGURE 6 - DENMARK: Recursive estimation for money demand
.04F
04--Innovsl                                      I t--RStep !  Ii
I 021I                                                    1    I !  i i j o W-. - a - .. ....    ...
.027          X         DE         :      _
021       1                                                       1~~~~~~~~~~~~0
i                                  I
r     . , I.1,  .,,I ....I
1985          1990                         1985          1990
F .- 5% _ l t jp 5~~~~~~~~5% :-  Ndn C HOWs|
l-                   ''''V 
0.      i             j.  I  .      .2   ..
1985          1990                         1985          1990
For a list of aDd explanation for the dummies used in the estimation, see Table A.5.



TABLE 7 - JAPAN: Single Equation for Money (Modeling A(m - p)),
Variable           Coefficient         StdL Error            t-value         Hansen Instability
Constant                   0.047               0.006                7.808                0.18
A(m - p),                 -0.251               0.058               4.320                0.35
A(m - p),                 0.116                0.049               2.395                 0.1
A(m - p),                 0.209                0.049               4.273                0.04
A(m - p),6                0.185                0.051               3.635                0.09
A(m-p),_9                 0.197                0.050               3.965                0.06
A(m- p), 10               -0.119               0.051               -2.317               0.17
^2 pt                     -0.895               0.108               -8.254               0.23
A2p -12                  -0.190                0.075               -2.520               0.05
Ayt                       -0.079               0.026               -3.013               0.36
[A                        -0.000               0.002               -0.130               0.23
AJ,0                      0.002                0.002               1.004                 0.4
japECMrm2,1               -0.012               0.002               -7.595                0.15
Sample: 1978:4-1997:12
R2 = 0.681371                     F(27,209) = 16.553 [0.0000]       a=0.0056
AR 1- 7 F( 7,202)        =   3.4515 [0.0016]**
ARCH 7 F( 7,195)         =  0.47052 [0.8551]
Normality Chi2(2)        =   1.3498 [0.5092]
HETERO F(39,169)         =   1.4382 [0.0608]
Hansen Instability Test Results: Variance: 0.188812 Joint (variance & coefficients): 3.49508
F-CRISIS (93,116) =   1.334 [0.0704]
FIGURE 7 - JAPAN: Recursive estimation for money demand
02   innovs                                            5
.01~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1I 
F                                   DI .  P
1985          0                   0 1
1985       1990        1995                 1985        1990        1995
5%5%                                               -Nu CHOW      NClWsl
.5
I   -.r4---                  ~~~~~~~25
1985       1990        1995                 1985        1990        1995
'For a list of and explanation for the dummies used in estimation, see Table A.5.
** Denotes significance at 1%.



TABLE 8 - KENYA: Single Equation for Money (Modeling Am )'
Variable         Coe                Std. Error          t-value      Hansen Instabia-y
Constant                4.498             0.978              4.597              0.12
Am, - 1                -0.168             0.058             -2.922              0.2
Am, - 3                0.231              0.056             4.162               0.16
Ap,                    0.129              0.098              1.325              0.1
Ay,                    -1.522             0.870             -1.750              0.07
AP,                    -0.002             0.001      t      -3.813              0.1
A_2                    -0.002             0.001             -2.435              0.02
A]O                    0.010              0.002      j      4.762               0.11
2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Ae, - 2                -0.139             0.056             -2.495              0.05
Aet - 3                -0.148             0.055             -2.663              0.39
KenECMnM2,1            -0.065             0.014              -4.616             0.08
KenECMgdpt-1           -0.191             0.041              -4.580             0.12
KenECMispr, 1          -0.016             0.003             -4.639              0.03
Sample: 1977:2-1996:12
2 = 0.473089           F(30,208)  6.2251 [0.00001     a=0.0201
AR 1- 7 F( 7,201)      =   1.2203 [0.2930]
ARCH 7 F(7,194)        =   1.3342 [0.2360]
Normality Chi2(2)      =  0.33438 [0.8460]
HETERO F(42,165)       =  0.69782 [0.91411
Hansen Instability Tests Results: Variance: 0.095519 Joint (variance & coefficients): 3.70518
F-CRISIS (58,106) = 1.0628 [0.3873]
FIGURE 8 - KENYA: Recursive estimation for money demand
_-I.~ ~ ~ ~~~~~~~~~
r nov Lf --3i ~.>Ssl t   >-  
.05                                      .025- 
j 0  0            5 2 l\!' /j0, ,4    ;    f   F   °  l/    l'   1 i  i   :    ; f0
-.05       [
1985       1990        1995                1985       1990        1995
2.5 .    5%  -- -CiOWs                       !_   5%    NdncHowsI
% I    ICOW
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _   ~~~~~~~755
1985       1990        1995               1985        1990       1995
'For a list of and explanation for the dummies used in estimation, see Table A.5.



TABLE 9 - MALAYSIA: Single Equation for Money (Modeling A(m - p) )A
Variable         Coefcient          Std. Error       t-value            Hansen Instability
Constant               -0.088            0.073             -1.205             0.09
A(m - p)              -0.206             0.075            -2.763              0.34
Np1                    -0.788            0.302            -2.614              0.14
A 2At 12              -0.478             0.203            -2.350              0.09
Ay1                    0.053             0.022             2.397              0.13
AI°                   -0.001             0.004            -0.165               0.1
AP,                    -0.004            0.003            -1.278              0.14
Ae,                    -0.034            0.093            -0.369              0.07
MysECMrM2,1            -0.003            0.001             -3.001             0.09
MysECMytrend1-.        0.039             0.021              1.832             0.09
MysECMIalt,-1          -0.002            0.003             -0.766             0.11
MysECMIown,.1          0.004             0.003              1.406             0.11
Sample: 1980:8-1996:12
R2 = 0.447342         F(25,171) = 5.5365 [0.0000]   a=0.0132
AR 1- 7 F( 7,164)        0.21144 [0.9825]
ARCH 7 F( 7,157)            1.881 [0.07603
Normality Chi2(2)          1.1753 [0.5556]
HETERO  F(36,134)        0.96657 [0.5303]
Hansen Instability Test Results: Variance: 0.221976 Joint (variance and coefficients): 4.65502
F-CRISIS(48,123) = 0.66033 [0.9483]
FGIGRE 9 - MALAYSIA: Recursive estimation for money demand
r  :.   .           .      J                                    ---- -- --
JI                                   ~~~~~~~~~~1
02-
1985          1990          1995           1985         1990          1995
5  up CHOWs~                  F      %-dnH
1.5
.5~~~~~~~~~~~~~7
25       I
1            I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~L
1985          1990          1995          1985          1990          1995
'For a list of and explanation for the dummies used in estimation, see Table A.5.



TABLE 10 - URUGUAY: Single Equation for Money (Modeling A(m - A)
Variable        Coefficient        Std. Error         t-value      Hansen Instability
Constant               0.529             0.067             7.846              0.27
A(m - p)t - 2          0.132             0.037             3.578              0.07
A(m-p)t-6              0.109             0.036             3.022              0.2
A(m - P)t -12          0.133             0.041             3.205              0.2
A2 p                  -0.875             0.094            -9.313              0.07
A2  --0.187                              0.068            -2.742              0.08
A2   8                 -0.169            0.077            -2.191              0.02
A2A-,                 .-0.238            0.084            -2.831              0.1
A2pi-to               -0.472             0.094            -4.998              0.04
A2p                   -0.351             0.092            -3.830              0.12
A2P,_12               -0.158             0.068            -2.322              0.07
Ay,                    0.066             0.042             1.565              0.28
AI(7 7                 0.001             0.000             2.189              0.1
Ae,                    0.483             0.065             7.424              0.4
Ae, 1                 -0.382             0.032            -11.960             0.23
Ae, 2                  0.084             0.024             3.471              0.69*
Aef_4                 -0.071             0.020   -3.493                       0.12
Aef 8                 -0.060             0.022            -2.777              0.19
UruECMrM2,             -0.040            0.006             -7.064             0.27
Sample: 1982:9-1997:12
R2 = 0.897336         F(37,146) = 34.49 [0.0000]     a=0.0147
AR 1- 7 F( 7,139)      =   1.2385 [0.2858]
ARCH 7 F( 7,132)       =   1.4988 [0.1730]
Normality Chi2(2)      =   1.7613 [0.4145]
Xi^2  F(55, 90)        =   1.2694 [0.1562]
Hansen Instability test results: Variance: 0.226654 Joint (variance & coefficients): 4.96548
F-CRISIS (38,108)= 2.2689 [0.0005] **
; For a list of and explanation for the dummies used in estimation, see Table A.5.
** Denotes significance at 1%.



5                       :       ::: ~~-    iN I                                     . -N
.e   r                  :   ~~-~-=-;   -                  I                                      O
e     0                        -             
i(  __,_;_0\
2>ih,                              _?                                            ,v
S:al L '_:-t'= . d.E~~
0                                                               U  -1              >
-                                                                   0-, .74 
0                                                               z =;i_                    s 
WC~ ~ ~ ~ ~ ~~~O I                                                                         ON7_47-
<  g ' <0                                           r~       -    - '~    t
0                                           :,C                                                     ,,,
Cu           =                               xt.o                      l-                                   c
O.  __3S 
>  's-;-'- g -'.- '>~~~~~~~~~~~O
0~C & .,''
07_;-!7 .  - ->
_  II'-,I.  111._
__ O -^ > ~ °



Table 11: Summary Table for Money Demand Stability Tests
Country                 Recursive Least        Hansen Test             F-Crisis
Squares
Chow Tests       Variance  Joint       Statistic    p-value
Chile                stablea               0.163      3.999      1.343      [0.09831
Colombia            I
Entire Sample       unstable              0.714*    4.758      0.572      [0.993]
1982:3-1989.12      stablet              0.086      4.29
Denmark              stable                0.102      4.238      1.154      [0.254]
Japan                stable                0.189      3.495      1.334      [0.070}
Kenya                stable                0.096      3.705      1.063      [0.387]
Malaysia             stable                0.222      4.655      0.66       [0.948]
Uruguay              stablea               0.227      4.965      2.269      [°0. 001]**
a The recursive estimation does not include the crisis period.
b The overall instability in the sample comes from the period after the crisis.
*,** Denotes significance at 5% and 1%, respectively.
Table 12: Summary Table for Price Equation Stability Tests
Country                 Recursive Least        Hansen Test             F-Crisis
Squares
Chow Tests       Variance  Joint       Statistic    p-value
Chile                Stable                0.298      5.674      0.963      [0.565;
Colombia             Stablea               1.035*     6.754      2.545      [0.000]**
Denmark| Stable                            0.259      4.813      0.754      [0.8881
Japan                 Stable               0.523*     7.961      0.855      [0.779]
Kenya
Entire Sample       Unstable              0.491*    4.327       1.439     [0.037]*
1980s                                                          0.748      [0.884]
1990s                                                          2.163      [0.001]**
Malaysia              Stable               0.355      5.315      0.736      [0.886]
Uruguay              Stablea               0.21       5.568      2.142      [0.001]**
a The recursive estimation does not include the crisis period.
*, ** Denotes significance at 5% and 1%, respectively.



TABLE 13- CHILESingle Equation for Prices (Mo                    ing A2p)
Variable    _     Coeffiient          StdError              t-value       Hansen Instability
Constant                 -0.077               0.072              -1.076               0.23
A2 PI - 2                -0.175              0.058               -2.996               0.03
A2m, - 2                 0.027               0.013               2.124                0.36
Ay,                      -0.016              0.012               -1.331               0.1
Al °                     -0.000              0.000               -2.438               0.1
MOI   6                  -0.000              0.000               -2.346               0.09
Ae,                      0.085               0.028               3.044                0.17
Ap,*                     0.799               0.247               3.238                0.11
A2 WI                    -0.020              0.014               -1.427               0.05
Aut                      -0.019              0.008               -2.383               0.06
ASp,                     0.004               0.008               0.559                0.16
ChiECMrM2,-,             -0.000               0.005              -0.068               0.27
ChiECMytrendt-,           0.004               0.010               0.374               0.23
ChiECMIown, l             0.000               0.000               1.978               0.25
ChiECMrPPPt-4            -0.009               0.003              -2.724               0.15
ChiECMrwaget 1            0.005               0.010               0.471               0.31
Sample: 1978:3-1993:11
R2= 0.724088   F(32,149) = 12.22 [0.00001
AR 1- 7 F( 7,142)       =   1.9657 [0.0638]
ARCH 7 F( 7,135)        =  0.72714 [0.6492]
Normality Chi2(2)       =  2.4586 [0.2925]
HETERO  F(47,101)       =  0.63269 [0.9591]
Hansen Instability Tests: Variance: 0.297906 Joint (variance & coefficients): 5.67416
F-CRISIS (77, 72)= 0.96332 [0.5648]
FIGURE 13 - CHILE: Recursive estimation for price equation
l                                                                               l~~~~~~~~~~~~~~~~0
r .Inoys                                 | --ReslStep
.02                      j0 
u2.-                                       0i                   4-W
-.02;                                                        wt
1985            1990                         1985           1990
5%  --U t CHOWSI                      __    %  -- -NdnCHOWs~
2
riF 
4                                              1  <   ,t.6
4-   4  ii  4     ~~~~i t 
.                                       6 ,
1985            1990                       1985             1990
* For a list of and explanation for the dummies used in estimation, see Table A.5.



TABLE 14 - COLOMBIA: Single Equation for Prices (Modeling  4A) _
Variable            Coeffiient           Std. Error             t-value         Hansen Instability
Constant                   -0.017                0.072                -0.234                 0.04
Ap, - 1                    0.405                 0.068                5.920                 0.05
Ap, -                      0.213                 0.074                2.864                 0.05
Ap, - 12                   -0.263                0.074               -3.556                 0.06
'AM, 1                     0.006                 0.034                0.164                 0.18
Ay,                        -0.000                0.011                -0.004                0.17
oAI                        0.000                 0.000                1.398                 0.27
AJ*                        -0.003                0.001               -2.811                 0.03
51,* 7                  -0.003                0.001               -2.706                 0.22
Apt*-2                     0.389                 0.247                1.572                 0.12
Ap*- 3                     0.743                 0.295                2.514                 0.09
)t*- 4                     -0.404                0.257               -1.569                 0.07
Ae,                        0.003                 0.028                0.122                 0.03
Aw,                        0.089                 0.042                2.144                 0.22
Au,                        -0.000                0.010               -0.032                 0.06
Asp,                       0.007                 0.006                1.215                 0.25
Co1ECMnwage,-1              0.034                0.020                 1.737                 0.04
ColECMnM2t-                -0.002                0.009                -0.216                 0.04
ColECMIownt,I               0.000                0.000                 1.102                 0.04
Co1ECMnPPP,-4              -0.006                0.003                -1.797                 0.04
ColECMIfort,1              -0.000                0.000                -0.685                 0.04
Sample: 1982:3-1998:6
R2 = 0.762338                      AR 1- 7 F( 7,146)          =  1.8252 [0.0866]
F(42,153) = 11.685 [0.0000]        ARCH 7 F(7,139)            =  1.7248 [0.1079]
Normality Chi2(2)         =  4.6588 [0.09741
HETERO  F(63, 89)         =  1.3777 [0.0816]
Hansen Instability Test Results: Variance: 1.0347** Joint (variance & coefficients): 6.754
F-CRISIS (70,83)=  2.5448 [0.0000] **
FIGURE 14 - COLOMBIA: Recursive estimation for price equation
. o,   I .   I               0,  r   ,  ~~~~~~~~~~~~~~~~~~~~~~~~~~~-- - - ----------.
101                                         .01 1
r.0 I-    5%0 -'                          1 |     5
1990            1995                         1990            1995
For  leplanatio5% fNd                                     enot
0                       II  t   JI  K !
1        ~~1990            1995                       1990             1995 
4.For a list of and explanation for the duimmies used in estimation, see Table A.5** Denotes significance at 1%.



TABLE 15 - DENMARK: Single E luation for Prices Modeling A2p)    _
Variable           Coefficient          Std. Error           t-value        Hansen Instabilit
Constant                   0.091               0.049                1.844                0.05
A2 P, _                   0.208                0.065               3.215                0.26
A2 m, 12                  0.024                0.009               2.720                0.55*
Ay,                       0.014                0.007               1.949                0.19
0l                        0.003                0.001               3.678                 0.4
AJA                      -0.000                0.001               -0.524               0.14
Ae,                       0.008                0.035               0.218                 0.1
Ap,*                      0.330                0.088               3.738                0.26
A.J*                      0.002                0.001               1.596                0.08
Au, - 5                   0.023                0.006               3.742                0.05
Au, 6                     0.022                0.006               3.468                0.38
Au, -12                   0.025                0.006               3.889                0.09
A2w,                      0.035                0.020               1.718                0.07
denECMrM2t-1              -0.007               0.003               -2.505                0.05
denECMinfl,-,             -0.004               0.002               -2.408                0.06
denECMrwage,-,            -0.005               0.001               -3.187                0.05
denECMrPPP,.l             -0.025               0.008               -3.258                0.04
denECMuip,-,               0.000               0.000       J       2.998                 0.06
Sample: 1977:3-1993:12
R2 = 0.852827                     AR 1- 7 F( 7,149)            0.94918 [0.47071
F(45,156) = 20.088 [0.0000]       ARCH 7 F( 7,142)             1.3522 [0.2303]
Normality Chi2(2)            5.3779 [0.0680]
HETERO F(63, 92)         =   1.0987 [0.33681
Hansen Instability Test Results: Variance: 0.258881 Joint (variance & coefficients): 4.81306
F-CRISIS(68, 88)= 0.75377 [0.8878]
FIGURE 15 - DENMARK: Recursive estimation for price equation
005                                       .s-  . 
oh              I                 ii    0                7j  I4I i'/i f , o t   : W 1 M    l ,f
.005lI-~~~~~~~~1    I  ,.0 5 
-01
1985           1990                       1985           1990
___  5%_0- lu CHOWs|         j---  5%   -NdnCHOWs|
.75,
2-7s                                     
A
0- :  ~    - ,      , S:V..  .25n  ,- 
1985           1990                       1985           1990
'For a list of and explanation for the dummies used in estimation, see Table A.S. * Denotes significance at 5%.



TABLE 16 - JAPAN: Single Equation for Prices (Mod eling A2p)
Variable           Coefficient          Std. Error            t-value        Hansen Instability
Constant                   0.005                0.010               0.459                 0.22
A2Pt -10o                 -0.083               0.048                -1.730                0.38
A2p( ,                    -0.140               0.048               -2.897                0.39
A2m,                       0.036               0.016                2.223                0.39
Ay,                        0.032               0.013                2.463                0.04
AL(A- 6                    0.002               0.001                2.919                0.06
AIti7t°2                  -0.001               0.001               -1.917                0.07
Al0I I                    -0.001               0.001                -2.345               0.19
AIt                       -0.001               0.001                -1.843               0.13
A[,b5                     -0.001               0.001                -2.187                0.13
Ae,                        0.018               0.007                2.558                 0.25
Ae,-5                      0.013               0.007                1.769                 0.19
Ae, 7                      0.015               0.007                2.005                 0.44
A*                         0,493               0.099                4.976                 0.14
Ap,*6                     -0,228               0.091                -2.500                0.05
Al?*                       0.296               0.086                3.437                 0.07
AI                        -0.000               0.001                -0.238                0.06
A2w,                       0.008               0.005                1.621                 0.22
Au,                       -0.002               0.006                -0.322                0.36
Asp,                      -0.001               0.005                -0.134                0.06
japECMrm2,-1               -0.005               0.001               -3.587                0.22
japECMrPPP1 4              -0.001               0.001               -1.684                0.26
japECMuip1i                0.000                0.000                3.106                0.08
japECMrwage,-,             0.004                0.002                2.070                0.07
Sample: 1978:4-1997:12            AR 1- 7 F(7,189)          =  1.5956 [0.1389]
R2 = 0.860978                     ARCH 7 F( 7,182)          =  0.36205 [0.9232]
F(40,196) = 30.346 [0.0000]       Normality Chi2(2)         =  1.1212 [0.5709]
HETERO F(65,130)          = 0.71114 [0.9365]
Hansen Instability Test Results: Variance: 0.523257* Joint (variance & coefficients): 7.96058
F-CRISIS (93,103)= 0.85501 [0.77861
FIGURE 16 - JAPAN: Recursive estimation for price equation
005~~~~~~~~~~~0
-   j                 -L-     _l   ----   .... ,, ...   _.  -.. 
1985       199(       1995             1985       1990       1995
S' -    °  ---  , CHOW3]           1 t     N-- Ndn CHIOWs,
151           ,        t     ~~~~~~~~~~~~~75
5-
|   1985   1990}       1995            1985        1990       1995



TABLE 17 - KENYA: Single E uation for Prices (Mod1eling Ap)
Variable           Coefficient          Std. Error           t-value         Hansen Instability
Constant                  -0.982               0.477               -2.058                0.06
AP' - 3                   0.093                0.040               2.342                0.01
AA - 4                    -0.107               0.038               -2.823               0.19
Ap, -                     0.091                0.045               2.005                0.04
Am, 3                     0.046                0.022               2.054                0.04
Am, - 4                   0.070                0.022               3.161                0.19
Ay,                       -0.034               0.391               -0.086               0.15
Al,                       0.001                0.000               2.108                0.02
AlA                       0.001                0.000               4.367                0.15
AlI0                      0.002                0.001               2.350                0.02
AI,0                      -0.003               0.001               -3.753               0.08
AIT,                      0.004                0.001               5.010                0.04
Al*                       0.001                0.001               1.265                0.07
Ae,                       0.047                0.019               2.411                0.01
Ap_ 2                     0.656                0.234               2.807                0.06
kenECMnM2W-                0.009               0.007                1.441                0.08
kenECMgdp,l                0.040               0.020                2.017                0.06
kenECMispr, 1              0.003               0.002                2.119                0.09
kenECMnPPP,l              -0.004               0.003               -1.603                0.02
kenECMidiff,_             -0.016               0.010               -1.568                0.03
Sample: 1977:2-1997:12
R2 = 0.833408                     AR 1- 7 F( 7,188)        =  0.37837 [0.9142]
F(43,195) = 22.687 [0.0000]       ARCH 7 F(7,181)          =  0.82656 10.5664]
Nonnality Chi2(2)        =  4.7828 [0.0915]
HETERO F(62,132)         =   1.211  [0.1807]
Hansen Instability Test Results: Variance: 0.491027* Joint (variance & coefficients): 4.32741
F-CRISIS-80s(58, 96)      =  0.74783 [0.8841]
F-CRISIS-90s(41, 96)      =  2.1629 [0.0011] **
FIGURE 17 - KENYA: Recursive estimation for price equation
.02  -.      _,             _      ..
.05                                         0-'
L   :   .   . .   !   .   ,I  ,         
1'~~~~~~~~0
025   ~ ~~UPHOWll98                                                            -
1985        1990         1995 1985                       1990         1995
________-___________|                  1.5 tz-  5%  ----NdnCHOWs   ,;
1[                       /
L           ,   .,,    i   |:    t         w~~~~~~~~~~~L 
1                                     viI1 0
I , I ~~~~~~~~.s                    , t..' 
1985        1990         1995               1985        1990         1995



TABLE 18 - MALAYSIA: Single E uation for Prices Modeling A                   _p) _
Variable          Coefficient         Std. Error           t-value       Hansen Instability
Constant                 0.009               0.027              0.337                0.27
A2pt _ 1                 -0.164              0.063              -2.596              0.04
A m, l                   0.008               0.011              0.762               0.45
Ay,                      -0.007              0.005              -1.378              0.04
°AI'                     0.000               0.001              0.256               0.07
Al4A                    -0.001               0.001              -2.679              0.06
Ae,                      0.050               0.022              2.283                0.21
Ae, - 3                  -0.058              0.021              -2.841               0.3
Ae, _ 5                  0.065               0.020              3.302               0.31
0.399              0.108               3.704               0.16
MysECMrM2t-1             -0.002              0.001              -1.213               0.23
MysECMytrendt-1          0.004               0.005              0.832                0.27
MysECM1alt, l            0.000               0.001              0.250                0.3
MysECMlown,_1            -0.000              0.001              -0.004               0.31
MysECMrPPPt-.            0.001               0.001              0.559                0.09
Sample:1980:8-1996:12
R2 = 0.703996           F(26,170)  15.551 [0.0000]
AR 1- 7 F( 7,163         =  0.80692 [0.5827]
ARCH 7 F(7,156)          =   1.0472 [0.4006]
Normnality Chi2(2)       =   4.9951 [0.0823]
1BETERO  F(40,129)    =   1.4135 [0.07611
Hansen Instability Test Results: Variance: 0.354895 Joint (variance & coefficients): 5.31541
F-CRISIS (48,122) =   0.7358 [0.8858]
FIGURE 18 - MALAYSIA: Recursive estimation for price equation
.01  K _ _                                       _ _ _ _
rK           l nn|vs                        ...... ResisteP      
oI                              I                                        g
.105~ .!)                                   005                      I
,    ,  ,     ,        oos                 I
1985          1990           1995          1985           1990           1995
2.5     I5%  -    CHOWs                              5%     NdnCHOWs
j         .75  _                           ,1
I .5            ,.A.                                          V
F                        . .
.5~~~~~H
.5~~~~~~~~~~~~~~~2
1985          1990           1995          1985           1990           1995
For a list of and explanation for the dummies used in estimation, see Table A.5



TABLE 19 - URUGUAY: Single Equation for Prices (Modeling A2p)                  _
Variable          Coefficient          Std. Error           t-value        Hansen Instability
Constant                  0.019               0.104               0.188               0.17
A2p -i                   -0.306              0.123               -2.491               0.16
A2 pI -2                 -0.385              0.098               -3.920               0.05
A2pP _                   -0.214              0.087               -2.473               0.04
A2pI - 4                 -0.190              0.069               -2.760               0.32
A2p - 5                  -0.238              0.050               -4.733               0.03
A2pt - 7                 -0.168              0.039               -4.301               0.18
A2 m, 5                  0.093               0.022               4.175                0.12
A2m, - 6                 0.090               0.028               3.264                0.19
A2 m  _ 7                0.105               0.028               3.691                0.09
A2m, - 8                 0.080               0.025               3.185                0.07
A2 m, 9                  0.050               0.020               2.446                0.07
Ay, - 4                  0.039               0.017               2.365               0.69*
A10                      0.001               0.000               2.877                0.05
AP,                      -0.001              0.000               -3.516               0.03
Al?- 8                   -0.001              0.000               -2.880               0.13
Au, - 1                  -0.051              0.012               -4.158               0.1
Au, -2                   0.053               0.013               4.125                0.1
AU, - 3                  -0.066              0.015               -4.430               0.08
Au, _ 6                  -0.039              0.014               -2.775               0.15
AU-7                      0.031               0.012              2.568                0.09
A2w,                     0.151               0.022               6.965                0.23
A2 w,                    0.110               0.020               5.578                0.56*
Ae,                       0.107              0.048               -2.240               0.04
Ae,                      0.118               0.018               6.663                0.04
Ap*                       1.053              0.409               2.575                0.11
A,*                      0.008               0.002               3.726                0.1
AI,*9                    0.005               0.002               2.557                0.2
uruECMrPPPt-1            -0.003               0.004              -0.774                0.1
uruECMuipt .              0.000               0.000               3.332               0.12
uruECMrM2 1               0.001               0.008               0.105               0.17
uruECMrwage,l             0.026               0.017               1.476               0.17
Sample: 1982:9-1997:12
R2= 0.875851             F(39,144) = 26.049 [0.0000]
AR 1- 7 F( 7,137)       =  0.95472 [0.4670]
ARCH 7 F(7,130)         =  0.75209 [0.62841
Normality Chi2(2)       =  0.24055 [0.8867]
HETERO  F(70, 73)       =   1.1522 [0.2748]
Hansen Instability Test Results: Variance: 0.210029 Joint (variance & coefficients): 5.56761
F-CRISIS (38,106) = 2.1422 [0.00121 **
'For a list of and explanation for the dummies used in estimation, see Table A.5.
*,** Denotes significance at 5% and 1%, respectively.



URUGUAY: Single Equation for Price (Modeling A2p), continulled
FIGURE 19 - URUGUAY: Recursive estimation for price equation
.02L,L   -vn c-                                    L 0 -  -- ReslSteP 
.02     n. -.
.02
i990              1995                          1990                1995
2     5%    -- lup CHOWg                      5|    %  -----Ndn CHOWs|
.   ,
1990              1995                          1990              1995



TABLE 20 - CHILE: Price equation with crisis interactionst
Variable       Coefficient  Std.Error        Interacted Variable        Coefficient  Std.Error
Constant          -0.123        0.099                      _
A pi-2            -0.105        0.095       A2 p   2*crisisdummy          -0.062         0.111
A2m, 2            0.036 *       0.017       A2m, - 2 *crsi dummy          -0.017         0.025
Ay,               -0.011        0.014       Ay, *crisis dummy             -0.016         0.013
AIJ°              -0.0001       0.0001      AI,0 5*crisis dummy           -0.0001        0.0001
AI- 6             -0.0002 *     0.0001      Al _ 6 *crisis dummy          0.0001         0.0001
Ae,               0.09          0.057       Ae, *crisis dummy             -0.026         0.068
Ap,*              0.978 **      0.316       Ap* *crisis dummy             -0.167         0.413
A w,                                        A, *crisis dummy
Au,               -0.019        0.012       Aut *crisis dummy              0.002         0.015
Asp,              -0.002        0.011       Asp, *crisis dummy            0.009          0.017
chiECMrM2t,-      0.001         0.008       ChiECMrM2,-. *crisis dummy     0.004         0.007
chiECMytrend- ,    0.015        0.014       ChiECMytrend1 -1* crisis dummy   -0.016      0.009
chiECMlown, -.    0.00002       0.0001      ChiECMlownti,*crisis dummy    0.0001         0.0001
chiECMrPPP -,    -0.009 *       0.004       ChiECMrPPP,1* crisis dummy    -0.005         0.003
chiECMrwaget-I1  -0.007         0.014       ChiECMrwage,-1*crisis dummy   0.019          0.02
tCrisis dummy equals one for the period 1981(1) - 1987(12) and it is zero otherwise.
TABLE 21 - COLOMBIA: Price equation with crisis interactionst             _-
Variable           Coefficient  Std.Error  Interacted Variable              Coefficient  Std.Error
Constant           -0.303*       0.119
Apt -             0.388**       0.099        Ap, - *crisis dummy           -0.023        0.121
Apt - ,           0.143         0.109        Ap, - *crisis dummy           0.092          0.119
Ap, -12           -0.258*       0.11         ZP, - 12 *crisis dummy        -0.029         0.118
Am, 1             0.004         0.039        Am,1- , *crisisdummy          -0.116        0.088
AY,               -0.013        0.013        Ay, *crisis dummy             -0.004        0.017
AIl'              0.001         0.0004    I* *crisis dummy                 -0.001        0.001
A*                -0.002        0.003        Ai, *crisis dummy             -0.000        0.003
! Al, 7 - .-0.003               0.002   A1,'            7 *crisis dummy    -0.0004        0.002
Apt*- 2           -0.304        0.383        4p-2*crisis dummy             0.967         0.519
Ap*3              0.588         0.399        4p  3       *crisisdummy      0.138         0.571
472,- 4           -0.341        0.37              4*crisisdummy            0.107         0.483
Ae,               0.036         0.031        Ae, *crisis dummy             -0.043        0.119
Aw,               0.117*        0.049        Aw, *crisis dummy             -0.081        0.111
Au,               -0.009        0.012        Au, *crisis dummy             0.031         0.022
Asp,              0.013         0.007        Asp, *crisis dummy            -0.013         0.019
ColECMnwage,-   0.042           0.03        ColECMnwage,-, *crisis dummy   -0.005        -0.095
CoIECMnM2,-       0.026 *        0.013       ColECMnM2,-*crisis dummy       0.006         0.010
ColECMIown,-,    0.0001          0.001       ColECMIown,-_*crisis dummy    0.001          0.001
ColECMnPPP,-,    -0.025 **       0.009       ColECMnPPP, ,*crisis dummy    0.010          0.D11
ColECMIfor,_,     -0.000         0.001       Co1ECMIfor, *crisis dummy      0.001         0.001
tCrisis dummy equals one for the period 1982(1) - 1987(12) and it is zero otherwise.
* Denotes significance at the 5% level.** Denotes significance at the 1% level.



TABLE 22 - DENMARK: Price equation with crisis interactionst
Variable            Coefficient   Std.Error          Interacted Variable         Coefficient   Std.Error
Constant           0.108           0.056
t2 p! _ !   | ~0.233**        0.073          2p      *rssdmY-0.003                          0.132
,-,                            .73A p1  ~crisis dummy
-2 n12             0.006         i 0.011         A2m, 12 *crisis dummy           0.009           0.011
AV, - 5            0.011           0.009         Ay, _5 *crisis dummy            0.015          0.019
AnsI',    ~        ~ 0.003**       0.001         AI°  *crisis dummy              -0.001          0.002
IAJ.4              -0.001          0.001         AA *        dm                  0.001           0.002
1~~~~~~~~~~~~P *crisis dummy
Ac,                0.014           0.039         Ae *crisis dummy               0.033           0.124
0.345**         0.11                   .                     0.060           0.173
Apy   _Ap1  *crisis dummy
_J*              0.002 *         0.001            *   .  d                    -0.004          0.002
Au                 0                             Au1  *crisis dummy
Al, - 5            0.02 **         0.007         Au, - 5 *crisis dummy           -0.0002         0.009
Au, -           _ 1 0.022 **       0.007         Au,26 *crisis dummy             -0.008          0.009
Llq-12       ~0.027 **        0.007         Au, -2 * crisis dummy           -0.006          0.010
A2 W,              0.048 *         0.022         A2 w, *crisis dummy             0.004           0.022
DenECMrM2,-,    -0.009 **          0.003         denECMrM2, ,*crisis dummy       0.003           0.002
DenECMinfl,.,      -0.005 *        0.002         denECMinfl [-I *crisis dummy    -0.005          0.009
DenECMrwage,-,   -0.003            0.002         denECMrwage,-j *crisis dummy   0.001            0.005
DenECMrPPPt.,    -0.041 **         0.011         denECMrPPP-,j*crisis dummy      0.042           0.031
DenECMuip, 1       0.0002 *        0.0001        denECMuip, *crisis dummy        0.001           0.0005
iCrisis dummy equals one for the period 1987(1) - 1992(12) and it is zero otherwise.
TABLE 23 - JAPAN: Price equation with crisis interactionst
Variable        Coefficient   Std.Error          Interacted Variable          Coefficient   StdError
I Constant           -0.005          0.012
2                 -0.100 *        0.051         A2p, _  *crisis dummy           0.265 **        0.089
A2A t              -0.171 **       0.052          A2p, , *crisis dummy            0.261 **        0.091
Am,                0079**          0.024          A2M,, *crisis dummy             -0.089**       0.033
Ay,                  0.018           0.019          Ay, *crisisdummy                0.013           0.026
I A 6 A              0.002           0.001           AA   *crisis dummy             0.001           0.002
A3 0 tI   2       |-0.001 *        0.001          L,I   *crisisdummy             -0.003           0.002
[ AI7               T -0.001 *       0.001          A1I,° ,, *crisis dummy          -0.0001         0.002
A b,b  -0.002 *                    0.001          AI1 b *crisis dummy             0.002           0.001
A3,i             T -0.002 **       0.001          AIb  *crisis dummy              0.002           0.001
Ae, - 2            0.024 **        0.009          Ae, 2 *crisis dummy             -0.019          0.016
Ae, - 5            0.022 *         0.009          Ae, -5 *crisis dummy            -0.019          0.015
Ae, 7              0.016            0.009         Ae1  *crisis dummy              -0.009          0.015
*t - 2         | 0.516**        0.109                                          -0.071          0.223
Ap- 2                                             An________    -2 * crisis dummy  -.7             .2
Ap                 -0.279 *        0.107          Ap   6 *crisis dummy            0.174           0.219
t*g            t 0.305          0.094           l*  5risis dummy               -0.122          0.211
Al,                 -0.0001        0.001          M**erisisdummy                  0.001           0.002
A2W,               0.008           0.005          A2w, *crisis dummy              0.0002          0.001
Au ,               -0.001          0.007          Au, *crisis dummy               -0.011          0.012
Asp,               -0.011           0.009         Asp *crisis dummy               0.011           0.011
JapECMrm21,-       -0.003           0.002        japECMrm2,-1*crisis dummy        0.001           0.002
sJapECMrPPP -     | -0.002*         0.001        japECMrPPP, -*crisis dummy       0.001           0.001
JapECMuip,-,        0.001 **        0.0002        japECMuip11*crisis dummy        0.0001          0.0004
LJapECMrwageL        0.006            0.003        japECMrwage,-*crisis dummy      -0.001          0.001
tCrisis dummy equals one forthe period 1990(1)- 1997(12) and it is zero otherwise.



TABLE 24 - KENYA: Price equation with crisis interactionst
Variable          Coefficient   Std.Error  Interacted Variable              Coefficientf Std.Error
Constant         -0.496         0.694
Ap, -            0.039          0.061       Ap, - 3 *crisis dummy          0.081          0.082
Ap,       -         4 0.012     0.063        Ap, - 4 *crisis dummy         -0.114         0.083
Ap, - 8          0.144 *        0.059        Ap, - 8 *crisis dummy          -0.128        0.094
Am, -            0.036          0.025        Am, - *crisis dummy            -0.022        0.055
At -, 4          0.049          0.026        Am, - 4 *crisis dummy         0.073          0.055
Ay,              0.165          0.481        Ayt *crisis dummy             -5.081 **      1.791
AiA              0.001          0.001       AlA  *crisis dummy             -0.001         0.001
A-               0.0004         0.001        AI, *crisis dummy             0.001         0.001
AI'0             0.001          0.001        A)'0 *crisisdummy             0.002          0.002
A,0              -0.002         0.002        AI02 *crisis dummy            -0.002         0.002
A106             0.003 *        0.001        AI)'  *crisis dummy           0.002          0.002
A'Vt             0.001         0.001         Als *crisis dummy             0.031 **       0.012
Ae,              0.025          0.033        Ae, *crisis dummy              0.047         0.044
Ap t - 2         0.633*         0.254        Ap   2 *crisis dummy          0.379          1.098
kenECMnM2 -,   0.005            0.009       kenECMnM2,-, *crisis dummy    -0.018          0.019
kenECMgdp,,    0.020            0.029       kenECMgdpt-,*crisis dummy       -0.012        0.007
kenECMispr ,-,   0.003          0.002        kenECMispr ,-,*crisis dummy    -0.002        0.001
kenECMnPPP t    -0.002          0.003        kenECMnPPP,.-, *crisis dummy   -0.054 *      0.022
kenECMidiff, ,   | -0.006       0.012        kenECMidiff,t-I*crisis dummy    -0.214 *     0.087
tCrisis dummy equals one for the period 1992(1) - 1995(12) and it is zero otherwise
TABLE 25 - MALAYSIA: Price equation with crisis interactionst
Variable          Coeff icient   Std. Error  Interacted Variable             Coefficient   Std. Error
Constant           0.016         0.028
A 2Pt - I         -0.153*       0.068        A 22p   *crisisdummy            0.139         0.168
| A2M, l         t0.002         0.011        A2m, .*crisis dummy             0.038         0.033
AY,               -0.009        0.006        Ay, *crisis dummy               -0.002        0.011
AIl?0             -0.001        0.001        Alo *crisis dummy               0.002         0.001
A_ 2              -0.002 **     0.001        AIA   *crisis dummy             0.001         0.001
Ae,               0.061 *       0.025        Ae, *crisis dummy               -0.029        0.053
Ae, -3            -0.052 *      0.023        Ae, - *crisis dummy             -0.007        0.056
Ae, - 5           0.038         0.022        Ae, - *crisis dummy             0.127 *       0.053
Ap,*-             0.399 **      0.124         Ap*  *crisis dummy             -0.149        0.277
mysECMrM2 t          0- 0 -°-°°1  0.001      mysECMrM2 t_,*crisis dummy       0.001         0.003
mysECMytrend,  -,  0.001         0.005       mysECMytrend, ,- *crisis dummy   -0.002        0.013
mysECMIalt t-      0.001         0.001       mysECMIalt t-,*crisis dummy     -0.002         0.002
mysECMIown t-1   -0.001          0.001        mysECMIown t-,*crisis dummy    0.002          0.002
mysECMrPPP ,-    0.001           0.001        mysECMrPPP t_,*crisis dummy    0.001          0.003
ICrisis dummy equals one for the period 1985(1) - 1988(12) and it is zero otherwise
* Denotes significance at the 5% level.
** Denotes significance at the 1% level.



TABLE 26- URUGUAY: Price equation with crisis interactionst
Variable          Coefficient  Std. Error  Interacted Variabie             Coefficient  Std. Error
Constant             -0.13      0.119
A2p -i           -0.213        0.185        A2p - *crisis dummy           -0.725 *      0.327
A2pt - 2         -0.246        0.167        A2pI - 2 *crisis dummy        -0.641 *      0.271
A 2pt - 3        -0.056        0.145        A2p  3 *crisis dummy          -0.619 *      0.245
A2 p             0.038         0.115        A2 p    *crisis dummy         -0.685 **     0.197
A2pt  5          -0.044        0.071        A2p- 5 *crisis dummy          -0.521**      0.155
A2pr - 7         -0.159 **     .0546        A2 p, - *crisis dummy         -0.067        0.102
A2 m, -5         0.061*        0.029        A2 m, - *crisis dummy         0.155*        0.062
A2 m - 6         0.088 *       0.036        A2 m, - 6 *crisis dummy       0.169*        0.075
A2m, - 7         0.065         0.036        A2m, 7 *crisis dummy          0.227**       0.072
A2mI - 8         0.023         0.036        A2m, 8 *crisis dummy          0.137 *       0.06
A22m1  9         0.021         0.028        A2m- 9 *crisis dummy          0.053         0.05
Ay, - 4          0.025         0.018        Ay, - *crisis dummy           0.049         0.059
Al0              0.001*        0.0003       A10 *crisis dummy             0.001         0.001
Al0 3* -0.001                  0.0003       A1° 3 *crisis dummy           0.0004        0.001
A1'              -0.0002       0.0003       AI-  s *crisis dummy          -0.001        0.001
Au, - 1          -0.04**       0.013        Au, _ 1*crisis dummy          -0.145 *      0.063
Au, - 2          0.052**       0.014        Au1 2 *jcrisis dummy          0.114         0.065
A?4-3            0.041 *      | 0.017     i     - 3*crisis dummy          -0.007        0.064
Au, - 6          -0.017        0.015        Au, 6 *crisis dummy           -0.125*       0.052
AU, - 7          0.033 **      0.012        Au, - 7 *crisis dummy         0.109         0.064
A2w,             0.131 **      0.032        A2w, *crisis dummy            0.049         0.055
A 2W, _          0.069 *       0.027        A2W, _ *crisis dummy          0.131 *       0.051
Ae,              -0.184        0.094        Ae, *crisis dummy             0.205         0.127
Ae, -            0.002         0.099        Ae, , *crisis dummy           0.090         0.108
Ap,*             1.555 **      0.449        Ap*, *crisis dummy            -0.997        1.857
AI*              0.006         0.003             **isis dummy             -0.0001       0.005
A1I,* 9          -0.000        0.003        AJ1* 9 *crisis dummy          0.004         0.005
uruECMrPPP,-1   -0.001          0.009       uruECMrPPPt-I*crisis dummy    -0.009         0.013
uruECMuip,-1      0.001 **      0.0002      uruECMuip,-, *crisis dummy     -0.001 *      0.0003
uruECMrM2, -    0.013           0.009       uruECMrM2, ,*crisis dummy    0.004           0.0053
uruECMrwage t -1  0.022         0.029       uruECMrwage t 1 *crisis dummy   0.036        0.041
tCrisis dummy equals one for the period 1981(7) - 1985(12) and it is zero otherwise.
* Denotes significance at the 5% level.
** Denotes significance at the 1% level.



Data appendix
CHILE
Data Sample: August 1977- November 1993
List of variables:
m=log of broad money (M2)
p=log of CPI prices
y=log of industrial production
10= interest rate on deposits from 30 to 89 days
p*=log of US prices
i*=US 6 months CD rate
e= Peso/Dollar exchange rate
w=log of wage index
u= log of unemployment rate
sp=share price index
Data frequency and data sources:
Monthly: -exchange rate (Pesos/US$), M2, Chilean consumer price Index, US consumer price index, US 6 months CD rate, interest rate on deposits from 30 to 89
days, and share price index. Source: International Financial Statistics (IMF).
-unemployment rate. Source: UN Monthly Bulletin.
Quarterly: -industrial production and wage index. Source Central Bank of Chile.
COLOMBIA
Data sample: January 1981-June 1998
List of variables:
m=log of broad money (M2)
p=log of CPI prices
y=log of industrial production
10= average interest rate for 90 day certificates of deposits
p*=log of US prices
i*=US 6 months CD rate
e= Peso/Dollar exchange rate
w=log of wage index
u= log of unemployment rate
sp=share price index
Data frequency and data sources:
Monthly: -exchange rate (Pesos/US$), Colombian consumer price index, US consumer prices index, US 6 months CD rate, and share price index.
Source: International Financial Statistics (IMF).
- M2 and interest rate for 90 day certificates of deposit. Source: Central Bank Monthly Bulletin.
-industrial production and wage index. Source: Central Bank sources and DANE monthly bulletin.
Quarterly: -unemployment rate. Source: Central Bank and DANE monthly bulletin.
DENMARK
Data sample: January 1976-December 1993
List of variables:
m=log of broad money (M2)
p=log of CPI prices
y=log of industrial production
1°= average deposit rate
1a= bond rate
p*=log of German prices
i*= German bond rate.
e= Krone/Deutsche Mark exchange rate
w=log of wage index
u= log of unemployment rate
Data frequency and data sources:
Monthly: -exchange rate (Krone/Deutsche Mark), M2, industrial production, Danish consumer price index, German Consumer Prices Index, Danish deposit
interest rate, Danish govemment bond yield, and German govemment bond yield. Source: International Financial Statistics (IMF).
-unemployment rate and wage index. Source: OECD Main Economic Indicators.
JAPAN
Data sample: February 1977-December 1997
List of variables:
m=log of broad money
p=log of CPI prices
y=log of industrial production
I°= average CD rate
V= gensaki rate
1= 10 year bond rate
l*= US bond rate
p*=log of US prices
l*= US bond rate.
e= Yen/Dollar exchange rate
w=log of wage index
u= log of unemployment rate



sp=share price index
Data frequency and data sources:
Monthly: -exchange rate (Yen/US$), Japanese consumer price index, US consumer price index, industrial production, US government bond yield,
and share price index. Source: International Financial Statistics (IMF).
-M2+CDs, Gensaki rate, CD rate, 1 0-year govemment bond, and nominal wage. Source: OECD Main Economic Indicators.
KENYA
Data sample: December 1975-December 1997
List of variables:
m= log of broad money
p= log of CPI prices
y= log of annually interpolated GDP
1°= average rate on deposits from 2 to 6 months
1= 90 day t-bill rate
I*= US 6 months CD rate
p*= log of US prices
e= Shilling/Dollar exchange rate
Data frequency and data sources:
Monthly: -exchange rate (Shillings/USS), M2, Kenyan consumer price index, US consumer price index, interest rate on deposits from 2 to 6 months,
and 90-day treasury bill rate. Source: International Financial Statistics (IMF).
Annual: -GDP. Source: International Financial Statistic (IMF).
MALAYSIA
Data sample: June 1979-December 1996
List of variables:
m=log of broad money
p=log of CPI prices
y=log of industrial production
1°= 3-month deposit interest rates for commercial banks
1= 3-month deposit interest rates for financial institutions
1*= US 6 months CD rate
p*=log of US prices
e= Ringgit/Dollar exchange rate
Data frequency and data sources:
Monthly -exchange rate (Ringgit/US$), M2, Malaysian consumer price index, US consumer price index, industrial production, and 3-month deposit interest rates
for commercial banks. Source: International Financial Statistics (IMF)
- 3-month deposit interest rates for financial institutions. Source: Central Bank monthly bulletin.
URUGUAY
Data sample: August 1981-December 1997
List of variables:
m=log of broad money
p=log of CPI prices
y=log of industrial production
1°= interest rate on one to six months deposits:
1*= US 6 months CD rate
p*=log of US prices
e= Peso/Dollar exchange rate
w= log of wages
u= log of the unemployment rate
Monthly: -exchange rate (Pesos/US$), M2, Uruguayan consumer price index, and interest rate on I to 6 months deposirs.Source: International Financial
Statistics (IMF).
-wage index. Source: Central bank bulletin.
Quarterly: -unemployment rate. Source: CEPAL "Economic Survey
-industrial production. Source: Central bank bulletin.



TABLE A.1 - Description of the Causes and Consequences of Banking Crises
Country          Crsis     Causes                                    Scope of crisis                    TOtQl lOSS            Consequencesfor monetary policy
Chile         1981-87      *   deep recession in 1981                In period 1981-83, the authorities  Central bank's       .   Significant rise in inflation (from 9.5% in
*   exchange rate crisis in 1982         intervened in 13 banks and 6       operational losses         1981 to 26.5% in 1985), due to unsterilized
*   deficient financial organization of the    nonbank financial institutions (with   reached 18% of GDP  financing of massive support programs.
oligopolistic banking system         78% of outstanding loans). 19% of   in 1985.                 Inflation remained above 10% until 1994.
*   unsustainable private financial deficits   loans were nonperforming at the end                .   Increase in level and volatility of money
of 1983.                                                     multiplier at the beginning of crisis.
Colombia      1982-88      *   banking system suffering from         The authorities intervened in 6 banks  Estimated loss is                     n.a.
structural weaknesses                and 8 finance companies. 15% of   approximately 5% of
*   strong deterioration in terms of trade in  loans were nonperforming in 1984-   GDP
1981                                 85. In 1985-86, some insolvent
banks were nationalized.
Denmark       1987-92      *   deep recession in the latter part of the    Loan losses over 1990-92             n.a.                              n.a.
1980s                               represented 9% of loans. 40 of the 60
.   rapid increase in monetary aggregates    problem banks were merged.
due to financial liberalization.
Japan         1992-        *   Uncontrolled financial liberalization  Problem loans reprcsented 9% of   Rescue costs          .   Rise in bank intermediation spreads
present          fostering sharp asset price inflation  GDP in 1996.                     probably higher than   .   Easing of monetary policy prompted by the
*   Expansionary monetary policy reflected                                  US$100bn.                  necessity to foster economic growth
in low interest rates, followed by
significant tightening in the proximity
of crisis
*   Sharp economic slowdown and falling
asset prices at the beginning of crisis
n.a. means that the information is not available.



TABLE A.1 - Description of the Causes and Consequences of Banking Crises, Continued
Country       Crisis       Causes                                    Scope of crisis                     Total loss             Consequencesfor monetary policy
period
Kenya         1985-89      *   Extremely high growth in the number    66% of loans of one third of the   Approximate losses of   .   Provision of large amounts of credit to distressed
1993-95          of financial institutions in the 1980s,    commercial banks were         failed local banks          banks was a major source of monetary expansion
with very low regulatory barriers to  nonperforming.                     estimated at Ksh 9 bln      and inflation, undermining macroeconomic
entry and low minimum capital        Between 1984 and 1989, 2 local banks  or $158 million.          stability in Kenya in 1992/3.
requirements                         and 10 non-bank financial institutions                     .   Depositors moved their money to more established
.   Extensive insider lending, often to  (NBFIs) were closed or taken over. In  The 1993 frauds cost     banks, which have had to lower their interest rates
politicians; gross mismatch between  1993/4, an additional 5 banks and 10   the Central Bank of      to absorb the sudden excess liquidity.
maturities of assets and liabilities  NBFIs were taken over by the Central   Kenya total of Ksh
.   Huge frauds in 1993, involving 3     Bank of Kenya, with 2 more local     10.2 bn or 3.2% of
Kenyan banks                         banks in 1996.                      1993 GDP.
*   Heavy reliance on deposits from
parastatals
.   Reliance on deposits from construction
companies working on govemment
projects which receive their money all
at once after the budget is approved.
Malaysia      195-88       X   Financial liberalization in the early 80s,  Nonperforming loans represented 32%   Reported losses  .   Re-imposition of controls on interest rates during
spurring credit growth and price bubble  of total loans in 1988.         equivalent to 4.7% of       1985-87.
.   Terms of trade deterioration in 1985-    As of August 1986, 24 deposit taking   GNP.            .   Flight to quality and cash
86, which induced the bubble burst   cooperatives were suspended;                               *   Rise in money multiplier at the beginning of the
.   Annual growth plummeted from 7% to   depositors have been able to recover                            crisis
negative 1% in 1985 and 0% in 1986;   just 1/5 of their deposits.                               .   Decrease in reserve and liquidity requirements
collateral shrunk in value below the                                                                with the purpose of reducing banks cost of funds
loan amount it is meant to secure
.   Declining deposits, shrinking loan
demand, sporadic and chockingly-tight
liquidity
•   Almost all Malaysian banks
overexposed to the country's weakest
sector: real estate
Uruguay       1981-87      .   Deep world economy recession starting   11% of loans were nonperforming in    Estimated costs of                        n.a.
in 1980s                             1986.                               recapitalizing banks
.   Collapse of the existing exchange rate                                   estimated at US$350
system, and sharp devaluation                                            million (7% of GNP).
*   High lcvels of debt in foreign currency
and soaring intemational interest rates                                  _
n.a. means that the information is not available.
Sources: Brownbridge(1998), Caprio and Klingebiel (1996), Dominioni and Licandro (1989), Geraghty (1987), Hausmann and Rojas-Suarez. (1996), Koskenkyla (1994), 1indgren, Garcia, and Saal (1996),
Machua (1986), Mbitiru (1986), Sheng, (1996), and Sundararajan and Balino (1991).



TABLE A.2 - Review of Money Demand and Inflation Studies
COUNTRY/A UTHORS           DESCRIPTION
CHILE
Matte and Rojas (1989)      This study estimates a reduced form money demand equation for a modified measure of MI commonly used in Chile,
MIA, for the period 1978-86. The purpose of this exercise is to explain the sudden drop in money demand
experienced by Chile in 1984. This study does not use cointegration analysis. The demand for real MIA is modeled as
a function of prices, output, and the average deposit rate (the outside rate of money). Lags of money, output, and the
deposit rate are significant in explaining money demand.
Apt and Quiroz (1992)       Using cointegration analysis and error correction modeling, this paper estimates a monthly money demand function
for MIA in Chile during 1983:1-1992:8. The demand for real MIA is modeled as a function of prices, output, and the
average deposit rate (the outside rate of money). The change in the exchange rate is also included as a determinant of
money demand. The fnal dynamic equation obtained is stable.
Herrera and Vergara (1992)    Using cointegration analysis and error correction modeling, this study finds a stable long-run money demand function
(MIA) for the period 1978:1-1991:1.
Martner and Titleman (1993)   This study analyzes the relationship between the real money demand (MIA), short-term interest rates (on deposits
from 30 to 89 days), the price level (CPI), and real income (GDP) for the period 1975-1991. The cointegration
analysis based on Johansen's method shows the existence of a stable long-run relationship for money (one
cointegrating vector).
COLOMBIA
Kamas (1995)                This paper investigates monetary policy effects under crawling peg in Colombia, during 1975-89. A five variable
VAR is estimated. The variables included are: domestic credit, foreign reserves, exchange rate, prices, and income
(proxied by industrial production). The results reveal that neither domestic credit nor the exchange rate appear to have
played much of a role in determining inflation. Inflation seems to be largely inertial and the result of demand shocks.
Rei-nnhack ~and Mondino(l ~988)  This paper estimates a money demand equation for the period 1977-1985. The variables included in this study are:
narrow money, interest rate on 90 day certificate of deposit, exchange rate, CPI, and real GDP.
Reinhart and Reinhart (1991)   The authors estimate a VAR of output (real GDP), prices (cpi), monetary aggregates, interest rates on 90 day deposits,
the exchange rate, general and minimum wages for manufacturing employees, and export coffee prices. Results
indicate that money is exogenous, and past fluctuations in inflation, money growth, and exchange rate help predict the
exchange rate. In the end, authors settle for a 6 variable system using growth rates of narrow money, real income
(GDP), consumer prices (CPI), average wages in manufacturing, nominal exchange rate, and the level of nominal
interest rate. According to this model, monetary policy explains a large share of the variability in inflation, wages, and
the exchange rate.
Herrero and Julio (1993)    This study finds evidence of cointegration between MI, CPI, GDP, and interest rates during 1955-91 and 1970-92.
The results show that money demand is stable. Tests are performed with annual and quarterly data, respectively.
Fullerton (1993)            This paper uses an ARMA process to study inflation in Colombia during 1967-1990. The variables used are: MI,
exchange rate, and CPI. The paper provides evidence that MI and exchange rates can affect inflation.



TABLE A.2 - Review of Money Demand and Inflation Studies
DENMARK
Juselius (1988)              This paper finds one cointegrating vector reflecting a long-run money demand function for period the 1974:1 to 1985:4.
The variables used are real M2, domestic real demand for goods and services, bond rate, and the deposit rate.
Juselius (1998)              Using recently developed statistical tools for analyzing cointegrated 1(2) data, this article models money (M2), income,
prices, and interest rates in Demnark. The final model describes the dynamic adjustment to short-run changes of the
process, to deviations from long-run steady states, and to several political interventions.
The error correction model obtained for real M2 is stable.
JAPAN
Corker (1990)                This paper estimates a demand function for broad money in Japan that explains secular trends in the income velocity of
broad money during the 1970s and 1980s and the acceleration in the decline of velocity during 1986-1988. The paper
concludes that wealth effects and the opportunity cost of holding broad money can explain the developments in velocity.
Frowen and Buscher (1990)    This paper estinates money demand functions for three alterative monetary aggregates: Ml, M2, and M2C (which
includes CDs in addition to M2). All monetary aggregates are deflated using CPI. The sample period is 1973:1 to 1987:4.
Other variables included are: Real GDP, call money rate, Gensaki rate, and Kokusai-rate. The authors implement both a
partial adjustment as well as a cointegration and error correction modeling framework. For MI there is evidence of
cointegration, while for M2 the evidence is ambiguous.
Hsiao and Fujiki (1998)      This study compares results from a cointegration analysis with those from a structural modeling of money demand. The
data covers the period 1963:3-1993:1. The variables used are: M2+CDs, real GNP, and nominal call rate. The monetary
aggregate and income measure appear to be cointegrated. The error-correction model does not improve on the ADL
approach.
Arize and Shwiff (1993)      The authors estimate a money demand function for Japan for the period 1973:1-1988:4 using M2, real GNP, 3-month
Gensaki, weighted average of the interest rate on three-month certificates of deposits, the guideline three month deposit
rate, real wealth, and the real exchange rate. The variables are 1(1) and the results show the existence of one cointegrating
vector. Money demand appears stable.
Yoshida (1990)               This paper estimates an error correction money demand function for the period: 1968:1-1989:1. The variables used in this
study are: real M2+CDs, real GNP, the coupon rate on 5-year debentures, and the coefficient of variation of Nikkei stock
average. One cointegrating relation is found between money and real income. Money demand appears stable.
Soejima (1996)               Using data from the period 1957:3 to 1994:1, the author studies whether there is a long-run relationship between real
GDP, money supply (MI), and the price level. This study concludes that real and nominal GDP series can be seen as
stationary processes around a deterministic trend with structural change, while Ml is non-stationary. The study indicates
that the cointegration between the three variables, which previous studies found, arises from a mispecification of the time
[-.._______________________________  series model, and that the instability in the demand function arises from the non-stationarity of MI.



TABLE A.2 - Review of Money Demand and Inflation Studies, Continued
Arize (1990)                This study estimates a money demand function for the periods 1973:2-1981:4 and 1973:2-1986:4 using OLS in a setup of
real adjustment analysis. The variables used are: real Ml, domestic real income, bond yield rate, and a number of dummy
variables. The split in the sample is used to determine the stability of the model after the fnancial innovation period. No
evidence of structural change is found.
Hoffmaister and Schinasi    This paper estimates a VAR of call money rate, growth of M2, real output gap, land price inflation, and the CPI over the
(1994)                      period 1986-1993. The study finds monetary factors are the most important variables behind asset price inflation. With
respect to the CPI, it seems that the capacity of monetary factors to influence the consumer price level has decreased due
to structural changes.
Sekine (1998)               This paper examines the demand for broad money in Japan from 1975 to 1994. In spite of the large shocks due to the
process of financial liberalization and the subsequent "bubble" economy of the 1990s, the paper confirms that a stable
money demand function can be found by taking proper account of the financial liberalization and the wealth effects.
MALAYSIA
Habibulla (1990)            This study tests whether wealth is a better proxy for the scale variable than current income for the Malaysian money
demand. The sample period analyzed is: 1960-1984. The variables employed in this study are: Ml, M2, M3, GNP
deflated by CPI, 3-month T-bill rate, rate of return on the monetary aggregates of interest, and the growth rate of CPI.
After using a partial adjustment mechanism to specify the models, the authors conclude that the scale variable should be
defined in terms of permanent income rather than current income.
Dhakal and Kandil (1993)    T     _iis paper tests the hypothesis that inflation is a monetary phenXomenonfor a group of Asian countries. The model
includes the following variables: CPI, Ml, industrial production index, money market interest rates, unit value of imports,
and foreign nominal interest rates. The results of OLS estimations for Malaysia show that changes in money supply are
important and significant in predicting prices.
Abdullah and Yusop (1996)    This paper investigates the causal relationship between money supply and inflation during the period 1970-92. The
variables used are: Ml, M2 and the inflation rate(CPI). After applying Granger methodology, results reveal that money
supply causes inflation independently of the monetary aggregate used.
Yusoff (1988)               This paper estimates six behavioral equations with the purpose of analyzing the effects of monetary policy on inflation,
balance of payments (BOP), and real output. The sample covers the period 1960-1981. The method of estimation is 2SLS
and non-linear 3SLS. The equation for inflation posits that inflation is a function of changes in money and changes in
foreign prices. Both factors appear to be significant in the model.
Chye and Semudram           The objective of this study is to appraise the monetarist and neo-keynesian hypotheses in their ability to predict inflation
in Malaysia. The paper covers the period: 1960-1986. The models employed were estimated by OLS and showed support
for the monetarist view of inflation.
KENYA  .   .                                                                                  _
Page (1993)                 This study estimates an equation relating inflation to income and money supply (M2) and an equation representing money
demand as a function of past income, expected inflation, and the T-bill rate. T he money demand function for MI appears
to be stable.



TABLE A.2 - Review of Money Demand and Inflation Studies, Continued
Ndung'u (I 997)             This paper estimates a VAR with money, the domestic price level, the exchange rate index, foreign price index, real
output, and the interest rate. The period analyzed is 1970-1993. One cointegration relationship is found among the
exchange rate, CPI, and foreign prices. Causality tests indicate that exchange rate changes and domestic rate of inflation
changes predict each other. The world rate of inflation does not predict the domestic rate of inflation. Tests for inflation
show a structural break in 1982, when the crawling peg exchange rate was adopted.
Chakrabarti (1992)          This study aims at investigating the influence of various factors on inflation in Kenya. The sample period is: 1972-1989,
and the variables employed are: CPI, World Bank's manufacturing unit value index (as a proxy for world price),
exchange rate, money per unit of output, nominal interest rate on savings deposits, wage variables, lagged CPI, oil price,
and the government budget deficit. The empirical framework is OLS. The monetary aggregate used is M2. Aside from
other results, this study verifies that both the stock of money and exchange rate changes influence prices.
Adam (1992)                 This paper studies money demand in the period 1973-1990. The variables included are: GNP adjusted for changes in
terms of trade, GNP adjusted for changes in total final expenditures, CPI, T-bill rate, official exchange rate vis a vis the
US dollar, and narrow money. Using a general to specific modeling approach with the ECM term included, the study
finds one cointegrating vector between income and money.
Adam (1992)                 This study covers the period: 1972-1990. VAR is estimated for each of 5 monetary aggregates, plus income, inflation, T-
bill interest, and exchange rate depreciation. The results indicate the existence of two cointegration vectors, one
representing a money demand function and the other a relationship between interest rates and inflation. Using recursive
estimation, the author confirms money demand stability.
Ndung'u (1993)              This paper investigates the determinants of inflation in the period 1970-91. The variables used are: monetary base, real
gross national income, and the annualized treasury discount rate. Two cointegrating vectors are found and the estimation
of an ECM model shows that money supply affects inflation, but not money demand. In the second stage, the model
employed allows for an open economy. The variables added are the nominal exchange rate and the foreign price index.
Three cointegrating relationships are found: a money demand function, a purchasing power parity function, and a third
vector, which is not identifiable.
Mwega and Killick (1990)    This study performs OLS regressions of changes in the CPI on the growth of real income, changes in money supply (M2),
changes in import prices, and changes in previous year's inflation rate. The estimation periods are 1971-82 and 1971-88,
respectively. The authors also estimate a short-run money demand function for 1973:3-1988:4, using Ml, M2 and M3 as
monetary aggregates. The results show that the government may be able to influence demand for money by shifting
interest rates. Money demand functions were found to be stable, but more so for narrow money that for broader
aggregates.
Darrat (1985)               This paper estimates money demand equations for Ml and M2 during 1969-78 using as variables: real GNP, CPI, and an
average of quarterly short-term interest rates in major OECD countries. Money demand functions are found to be stable.
URUGUAY
Graziani (1988)             This paper tests an inflation model where inflation is modeled as a function of lagged money, output, interest rates,
export, and im.port prices The sample perind rnncidrepd is 1952198 Ind the metthod of estimation is OT S. Money and
import prices seem to have the largest effect on inflation.



Table A.3: Unit Root Tests
Country     Variable      t-adf       beta        sigma      lag        t-prob      F-prob
Chile       m              -2.724     0.966       0.029      3.000      0.001       0.079
p              -2.061     0.976      0.008       1.000      0.000       0.386
y              -2.071     0.919      0.039       12.000    0.063        0.081
w              -4.374**    0.899     0.022       10.000    0.001        0.1189
u              -1.557     0.975      0.070       0.000      -           0.5223
e             -0.889      0.993      0.022       1.000      0.000       0.3969
i°            -5.495**    0.705      8.805       0.000      -           0.3034
m-p           -2.109      0.974      0.029       3.000      0.000       0.194
w-p            -2.532     0.928      0.026       1.000      0.006       0.187
p-e           -1.223      0.990      0.021       1.000      0.000       0.577
dm            -5.288**    0.392      0.029       2.000      0.001       0.223
dp            -7.596**    0.488      0.008       0.000      -           0.465
dy            -2.918      -0.423     0.038       13.000    0.002        -
dw            -2.508      0.387      0.022       9.000      0.028       0.140
du            -12.982**  -0.003      0.071       0.000      -           0.666
de            -7.037**    0.543      0.022       0.000      -           0.507
di°           -12.680**  -0.365      9.175       1.000      0.000       0.199
d(m-p)         -9.953**   -0.180     0.027       1.000      0.781       0.056
d(w-p)         -17.310**  -0.223     0.024       0.000      -           0.346
d(p-e)        -7.467**    0.499      0.021       0.000      -           0.764
d2m           -8.735**   -3.821      0.029       6.000      0.012       0.317
d2p           -8.589**   -1.801      0.009       5.000      0.012       0.727
Colombia   m               -2.103     0.971       0.013      12.000    0.007        0.198
p              -2.100     0.983      0.006       11.000    0.017        0.213
y              -2.336     0.869      0.033       2.000      0.001       0.389
e             -0.426      0.998      0.014       1.000      0.000       0.204
io            -3.423      0.913       1.119      3.000      0.002       0.181
w              -1.437     0.961      0.014       13.000    0.001        -
u              -1.456     0.917      0.074       0.000      -           0.569
m-p            -1.751     0.975      0.015       1.000      0.001       0.252
w-p            -2.223     0.919      0.016       12.000    0.000        0.139
p-e           -1.062      0.995      0.014       1.000      0.000       0.578
dm             -3.848*    0.281      0.013       11.000    0.014        0.155
dp            -3.005      0.578      0.006       9.000      0.079       0.082
dy            -15.794**  -0.942      0.033       1.000      0.000       0.157
de            -9.59**     0.326      0.014       0.000      -           0.259
di'           -9.769**    0.274       1.161      0.000      -           0.140
dw            -2.580      -0.306     0.014       12.000    0.001        0.053
du            -8.539**   -0.078      0.073       0.000      -           0.827
d(m-p)        -10.519**  0.230       0.015       0.000      -           0.356
d(w-p)        -3.452*    -0.518      0.016       11.000    0.000        0.410
d(p-e)        -9.221**    0.360      0.015       0.000      -           0.656
For each variable the columms report the augmented Dickey Fuller (ADF) statistic on the final equation (t-adf),
the estimated coefficient on the lagged level that is being tested for a unit value (beta), the estimated equation
standard error (sigma), the lag length of the ADF regression (tag), the tail probability on the longest lag of the final
regression (t-prob), and the tail probability of the F-statistic for the lags dropped (F-prob).
Rejection of the null hypothesis of a unit root is denoted by * and ** forthe 5% and 1% levels.
Definition of variables: m is a measure of broad money,p refers to the CPI, y is a measure of
output, w is a measure of wages, u is a measure of the unemployment rate, e is the exchange rate
(usually in termns of domestic currency per US$),i° represents the own rate of m2, iA represents the
alternative (outside) rate, i represents the foreign interest rate. All variables except interest rates are in logs.
Notation: d refers to the change in the variable. dZ refers to the second difference of a variable.



Table A.3: Unit Root Tests, continued
Country    Variable     t-adf      beta      sigma     lag       t-prob     F-prob
Dennark   m             -1.739     0.981     0.013      12.000    0.005      0.198
p            -1.304     0.995     0.004     0.000     -          0.644
y            -2.787     0.881     0.028      1.000    0.000      0.127
e(kr/dm)     -2.076     0.980     0.007      1.000    0.001      0.2445
p*(german)    -1.284    0.993     0.003      1.000    0.000      0.0576
i°           -1.484     0.978     0.327     0.000     -          0.082
iA           -2.770     0.951     0.543      12.000    0.005     0.489
i*(german ib)  -2.773   0.963     0.231      11.000    0.008     0.395
w            -1.294     0.988     0.007      12.000    0.008     0.894
u            -3.749*    0.935     0.030      12.000    0.000     0.066
m-p          -2.357     0.971     0.014      12.000    0.005     0.469
w-p          -0.855     0.983     0.008      11.000    0.033     0.162
p-e          -1.527     0.969     0.009      0.000    -          0.591
dm           -2.378     0.450     0.013      11.000    0.010     0.545
dp           -12.090**  0.123     0.004      0.000    -          0.842
dy           -13.628**  -0.618    0.028      1.000    0.005      0.153
de(kr/dm)    -10.710**  0.232     0.008      0.000    -          0.274
dp (german)  -9.408**   0.358     0.003      0.000    -          0.180
di"          -11.774**  0.195     0.317     0.000     -          0.687
diA          -4.408**   0.112     0.553      11.000    0.002     0.227
di*(german)   -9.314**   0.392    0.236     0.000     -          0.286
dw           -3.787*    -0.336    0.007      11.000    0.005     0.347
du           -2.688     0.479     0.030      12.000    0.003     0.796
d(m-p)       -2.350     0.484     0.014      11.000    0.005     0.777
d(w-p)       -5.675**   -1.110    0.008      10.000    0.026     0.196
d(p-e)       -11.974**  0.125     0.009      0.000    -          0.709
d2m2         -6.669**   -6.516    0.014      10.000    0.000     0.466
d2p          -8.265**   -5.683    0.004      10.000    0.003     0.651



Table A.3: Unit Root Tests, continued
Country    Variable     t-adf      beta      sigma       lag      t-prob      F-prob
Japan      m             -0.593    0.997      0.006      13.000    0.001      -
p            -2.611     0.980      0.004     12.000    0.008      0.720
y            -1.904     0.973      0.013     9.000     0.000       0.597
e            -2.466     0.964     0.029      11.000    0.035      0.856
Sp           -0.656     0.995     0.039      9.000     0.027      0.348
iA           -2.882     0.968     0.286      3.000     0.000      0.109
;°           -2.518     0.969     0.315      2.000     0.064      0.068
ib(1yOrOECD)  -3.150    0.925     0.373      0.000     -          0.164
w            -2.127     0.917      0.018     13.000    0.004       -
u            -0.852     0.986      0.033     2.000     0.000      0.434
m-p          -0.934     0.993      0.007      13.000    0.020      -
w-p          -1.855     0.838      0.019      13.000    0.005      -
p-e          -2.279     0.962      0.029     11.000    0.037      0.839
dm           -2.474     0.508      0.006     13.000    0.009      -
dp           -2.525     0.514      0.004     11.000    0.014      0.370
dy           -3.209     0.276      0.013     8.000     0.001      0.320
de           -10.513**  0.336      0.029     0.000     -          0.377
dsp          -4.279**   0.293     0.039      11.000    0.048      0.624
diA          -4.815**   0.560     0.288      4.000     0.020      0.108
dio          -9.341**   0.452      0.324     0.000     -           0.062
dib(1OyrOECD) -14.481**  0.053     0.382     0.000     -          0.130
dw           -3.997*    -2.657     0.018     13.000    0.009       -
du           -15.957**  -0.527    0.033       1.000    0.000       0.422
d(m-p)       -2.489     0.551      0.007      13.000    0.004      -
d(w-p)       -4.214**   -3.212     0.018      13.000    0.022      -
d(p-e)       -10.679**  0.322      0.029     0.000     -          0.330
2
d2m          -5.893**   -7.453     0.006      12.000    0.002     0.961
d2P          -8.242**   -7.059    0.004      10.000    0.002      0.722
Kenya      m             -0.694    0.992      0.024      12.000    0.007      0.860
p            -1.462     0.990     0.015      3.000     0.000      0.619
y            -2.015     0.980      0.007     0.000     -           0.937
e            -3.276     0.965     0.028      5.000     0.005      0.238
j°           -3.420     0.947     0.783      2.000     0.000      0.211
A            -4.334**   0.893     2.093      11.000    0.003      0.871
i* (US)      -2.838     0.954      0.552      13.000    0.004      -
m-p          -2.502     0.945      0.027      12.000    0.004      0.365
p-e          -2.567     0.917     0.034      12.000    0.007      0.449
dm           -3.796*    0.162     0.024      11.000    0.011       0.411
dp           -3.208     0.545     0.016      13.000    0.047      -
dy           -16.378**  -0.023    0.007      0.000     -          0.876
de           -4.525**   0.574     0.029      4.000     0.019      0.131
di'          -4.647**   0.259     0.794      13.000    0.028      -
diA          -5.719**   0.274     2.195      8.000     0.002      0.090
di* (US)     -4.160**   0.100     0.561      12.000    0.009      0.297
d(m-p)       -7.026**   0.200     0.028      2.000     0.001      0.066
d(p-e)       -6.466**   -0.618    0.034       13.000    0.028      -



Table A.3: Unit Root Tests, continued
Country    Variable     t-adf      beta       sigma      lag      t-prob      F-prob
Malaysia   m             0.424      1.004     0.014      0.000     -          0.733
p            -1.909     0.982      0.004      4.000    0.011       0.087
y            -4.253**   0.748      0.045      1.000    0.000       0.265
e            -1.463     0.974      0.010      13.000    0.011      -
i°,          -2.197     0.969      0.386     2.000     0.016       0.123
iA           -2.825     0.944      0.481      10.000    0.001      0.401
i* (US 6m cd)  -2.920   0.934      0.496      13.000    0.000      -
m-p          -0.231     0.997      0.015      0.000     -          0.828
p-e          -0.858     0.989      0.010      13.000    0.004      -
dm           -15.361**  -0.129     0.014      0.000    -           0.891
dp           -2.187     0.674      0.004      13.000    0.018      -
dy           -23.614**  -0.510     0.047      0.000     -          0.078
de           -4.378**   -0.116     0.010      12.000    0.010      0.378
di°          -11.584**  0.184      0.392      0.000    -           0.142
diA          -3.679*    0.300      0.491      9.000     0.005      0.435
di*(US 6m cd)  -3.802*    0.263    0.501      12.000    0.001      0.947
d(m-p)       -15.631**  -0.145     0.015      0.000     -          0.966
d(p-e)       -3.731*    0.211      0.010      12.000    0.003      0.151
d2m          -7.824**   -4.958     0.015      8.000    0.046       0.560
d2p          -6.601**   -5.783     0.004      12.000    0.009      0.178
Uruguay    m             -0.533    0.995      0.031      6.000     0.013      0.100
p            0.066      1.000      0.016      8.000     0.000      0.572
y            -2.938     0.758      0.045      0.000     -          0.326
e            -1.439     0.979      0.057      0.000     -          0.948
i°           -2.162     0.971      2.958      4.000     0.021      0.086
i((US cd rate)  -2.491  0.958      0.328      5.000     0.034      0.128
w             -0.668    0.994      0.028      8.000     0.000      0.735
u            -2.814     0.648      0.099      0.000     -          0.071
m-p          -2.634     0.926      0.038      0.000     -          0.315
w-p          -3.747*    0.894      0.024      4.000     0.000      0.389
p-e          -5.666**   0.853      0.053      0.000     -          0.627
dm           -3.657*    0.463      0.032      4.000     0.016      0.079
dp           -2.608     0.752      0.016      7.000     0.000      0.653
dy           -10.690**  -0.352     0.045      0.000     -          0.979
de           -13.792**  -0.059     0.058      0.000     -          0.819
dio          -7.063**   0.260      2.981      3.000     0.012      0.102
di*(US cd)    -8.905**   0.388     0.332      0.000     -          0.202
dw           -2.586     0.678      0.027      7.000     0.000      0.797
du           -9.967**   -0.452     0.098      0.000     -          0.136
d(m-p)       -13.846**  -0.062     0.039      0.000     -          0.236
d(w-p)       -4.842**   0.172      0.025      3.000     0.000      0.304
d(p-e)       -14.772**  -0.128     0.057      0.000     -          0.982
d2m2         -6.717**   -7.383     0.031      10.000    0.043      0.900
d2p          -10.483**  -3.628     0.016      6.000     0.000      0.483



Table A.4- Cointegration Results: Lag Length Selection and Eigenvalue Statistics - MONETARY SECTOR
Maximal Eigenvalue                  Eigenvalae Trace
Statistic,                        StaJlstic
Selected System    Ho:   Statisti  adjustedfor 95% critical   S       adjustedfor 95% critical
Lag Length   Rank=p              degrees of   value        S        degrees of  value
freedom                           freedom
Chile            3         p=O   70.46**      65.81**     31.5         125.6**    117.3**   63.0
p<=l  27.95*      26.11*      25.5         55.12**    51.48**   42.4
p<=2  16.46       15.37       19.0         27.16*     25.37*    25.3
p<=3   10.71       10.00      12.3         10.71     10.00       12.3
Colombia        13         p=O   29.66        21.79       31.5         74.98**   55.09      63.0
p<=l  26.04*      19.13       25.5         45.32*    33.3       42.4
p<=2   13.52      9.93        19.0         19.29     14.17      25.3
p<=3   5.771      4.24        12.3         5.771     4.24       12.3
Denmark          12        p==O  96**         67,49*4     37.5         191**     134.3**    87.3
p<=l  61.35**     43.13**     31.5         94.98**   66.77*     63.0
p<=2  20.09        14.212     25.5         33.63     23.64      42.4
p<=3   10.84      7.622       19.0         13.54     9.515      25.3
p<=4  2.692        1.893      12.3         2.692     1.893       12.3
Japan            7          p==O   54.74**    46.66**     37.5         119.9**   102.2**    87.3
p<--l  30.65      26.13       31.5         65.21    55.58      63.0
p<=2  22.39        19.09      25.5         34.56     29.45      42.4
p<=3  9.67        8.242       19           12.16     10.37      25.3
p<=4  2.493       2.125       12.3         2.493     2.125       12.3
Kenya           11         p=O  41.12*        32.42       37.5         117.2**   92.41t     87.3
p<=]  32.79*      25.85       31.5         76.08**   59.98      63
p<=2  27.11*      21.38       25.5         43.29*    34.13      42.4
p<=3   12.43      9.798       19           16.18     12.75      25.3
p<=4  3.749       2.956       12.3         3.749     2.956       12.3
Malaysia         3         p=O   56.78**      52.46**     37.5         163**     150.6+*    87.3
p<=l  46.42**     42.88**     31.5         106.3**   98.16**    63.0
p<=2  31.84**     29.41k      25.5         59.83**   55.28**    42.4
p<=3  21.92*      20.25*      19.0         28*       25.86*     25.3
p<=4  6.074       5.611       12.3         6.074     5.611      12.3
Uruguay          4         p==O   43.22**     39.46**     31.5         76.18**   69.56*     63
p<=l  20.19        18.44      25.5         32.96     30.09      42.4
p<=2   10.75      9.818        19          12.77     11.66      25.3
p<=3  2.016        1,84       12.3         2.016     1.84        12.3



Table A.4 - Cointegration Results: Lag Length Selection and Eigenvalue Statistics - EXTERNAL SECTOR
Maxima! Eigenvalue                  Eigenvalue Trace
Statistic,                        Statistic,
Selected System    Ho:   Statisi   adjustedfor 95% critical   Statisti  adjustedfor 95% critical
Lag Length   Rank'=p             degrees of   value                 degrees of  value
freedom                           freedom
Chile            5         p==0  43.49**      39.9**      25.5         68.51**   62.86**    42.4
p<=]  23.12*      21.21*      19           25.02     22.96      25.3
p<=2   1.905      1.748       12.3         1.905     1,748      12.3
Colombia         5         p=(0  57.93**      50.54**     37.5         129.9**   113.3**    87.3
p<=l  27.35       23.86       31.5         71.98**   62.8       63
p<=2  21.24       18.53       25.5         44.63     38.94      42,4
p<=3   17.03      14.86       19.0         23.38     20.4       25.3
p<=4.  6.355      5.545       12.3         6.355     5.545      12.3
Denmark          12        p= 0  58.94**      41.43*      37.5         140.5**   98.74**    87.3
p<= 1 32.74*       23.01       31.5         81.52**   57.31      63.0
p <= 2  29.03*     20.4        25.5         48.78*    34.29      42.4
p <= 3  17.83      12.54       19           19.76     13.89      25.3
p<= 4  1.923       1.352       12.3         1.923     1.352      12.3
Japan            6         p=  0 79.87**      69.76**     37.5         167**     145.9**    87.3
p<= 1 51.43**    44.92**       31.5         87.17**   76.13**    63.0
p<= 2  22.23       19.41       25.5         35.74     31.21      42.4
p<= 3  9.764       8.528       19.0         13.51     11.8       25.3
p <= 4  3.747      3.273       12.3         3.747     3.273      12.3
Kenya            12        p= 0  46.41"'    35.7          37.5         123.8**   95.22*     87.3
p<= 1 36.45*       28.04       31.5         77.38**   59.52      63,0
p<= 2  22.31       17.16       25.5         40.93     31.48      42.4
p <= 3  13.46      10.36       19.0         18.62     14.32      25.3
p<= 4  5.161       3.97        12.3         5.161     3.97       12.3
Malaysia         6         p=  0 42.77**      39.19**     25.5         64.94*t   59.51**    42.4
p<= 1 18.58        17.02       19           22.17     20.32      25.3
p<= 2  3.592       3.291       12.3         3.592     3.291      12.3



Table A.4 - Cointegration Results: Lag Length Selection and Eigenvalue Statistics - WAGES
Maximal Eigenvalue                   Eigenvalue Trace
Selected System    Ho:             Statistic,    95% critical          Statistic,    95% critical
Lag Length    Rank=p              d           value         Statistic  adjsted for value
Chile            4         p=  0  46.82**      43.73**     25.5         68.42**   63.91**    42.4
p<= 1  15.15        14.15       19          21.6      20.18       25.3
p<= 2  6.457       6.032       12.3         6.457      6.032      12.3
Colombia         2         p   0  29.18*       28.29*      25.5         45.24*    43.85*     42.4
p<= 1  11.12        10.78      19           16.05      15.56      25.3
p <= 2  4.939      4.787        12.3        4.939      4.787      12.3
Denmark          13        p=  0  26.88*       21.69       25.5         50.59**   40.82      42.4
p<= 1 23.31*       18.81        19          23.71      19.13      25.3
p<= 2  0.392       0.3163       12.3        0.392      0.3163     12.3
Japan            13        p=  0  94.87**      79.26**     25.5         120.8**   100.9**    42.4
p<= I  19.45*       16.25       19          25.89*    21.63       25.3
p-= 2  6.441       5.381        12.3        6.441      5.381      12.3



TABLE A.5 - Dummy Variables Used in the Cointegration Analysis and Single Equation Estimations
Country   |Dmmy Variable  lExplanationforincludingthedummy                          Dummy is included in...
Chile       December-79       Dummy controls for redefinition of money.             Monetary sector (MCI), labor
sector (WCI), and extemal
sector (ECI) cointegration
analysis and the money (M)
and price (P) equations (eqn.)
June-82           Dummy controls for exchange rate depreciation.        MCI, WCI, ECI, M, and P eqn.
July-82           Dummy controls for exchange rate depreciation.        MCI, WCI, ECI, M, and P eqn.
September-84      Dummy controls for exchange rate depreciation.        MCI, WCI, ECI, M, and P eqn.
October-84        Dummy controls for exchange rate depreciation.        MCI, WCI, ECI, M, and P eqn.
November-84       Following the September 1984 devaluation, the Central Bank P eqn.
transferred significant amount of resources to commercial
banks.
Colombia   Centered Seasonals Needed to control for changes in wage seasonality occuring
after 1990.                                           WCI and P eqn.
Interacted Centered
ISeasonais        Needed to control for changes in wage seasonality occuring
after 1990.
WCI and P eqn.
Denmark    DVAT               DVAT=3 for 1977Q4, 2.25 for 1978Q4, and 1.75 for 1980Q3
This dummy controls for three increases in the value-added ta)
rate.                                                 MCI, WCI, ECI, M, and P eqn.
DPRSTOP
DPRSTOP= I for 1978Q4, for 1979QI, for 1979Q4, and for
1980QI. This dummy controls for 4 periods of price controls. MCI, WCI, ECI, M, and P eqn.
DCOTAX            DCOTAX= I for 1979Q3, and for 1986Q2. This dummy
controls for two cases of special commodity taxes.    MCI, WCI, ECI, M, and P eqn.
DUMCAPCON         DUMCAPCON=I for 1983(1) through 1998(2); zero
otherwise; it controls for the removal of capital controls.  MCI and ECI
Oct-77            Controls for unidentified data outlier.               P eqn.
April-78          Controls for unidentified data outlier.               P eqn.
May-78            Controls for unidentified data outlier.               P eqn.
October-78        Controls for unidentified data oudier.                P eqn.
August-80         Controls for unidentified data outlier.               P eqn.
January-83        Controls for unidentified data outlier.               P eqn.
1983Q1            Controls for the time it takes for country to adjust to lifting of
capital controls.                                     MCI, ECI, M, and P eqn.
1983Q2           Controls for the time it takes for country to adjust to lifting of
capital controls.                                     MCI, ECI, M, and P eqn.
April-86          Controls for pressures in the foreign exchange market which  P eqn.
led to a rise in long- and medium-term interest rates, starting ir
the second quarter of 1986.
1992Q4            Controls for speculative attacks in the last quarter of '92.  MCI, ECI, M, and P eqn.
December-92       Controls for speculative attacks.                     MCI, ECI, M, and P eqn.
June-93           Controls for speculative attacks.                     MCI, ECI, M, and P eqln.
July-93           Controls for speculative attacks.                     MCI, ECI, M, and P eq[n.
August-93         Controls for speculative attacks.                     MCI, ECI, M, and P ecln.
Japan       Dmay              Dummy for May 1990, controlling for a shift from postal
savings into M2.                                      MCI, M, and P eqn.
Dapril            Dummny for April 1990, controlling for a shift from postal
savings into M2.                                      MCI, M, and P eqn.
April-89          Dummy controls for VAT increase.                      MCI, WCI, ECI, M, and P eqn.
April-97          Dummy controls for VAT increase.                      MCI, WCI, ECI, M, arid P eqn.
DjulyT            Dummy controls for bonus payments.                    WCI and P eqn.
DJuneT            Dummy controls for bonus payments.                    WCI and P eqn.
Notation: MCI, WCI, and ECI indicate that the corresponding dummy was allowed to enter unrestrictedly in the money, labor,
and external sectors cointegration analysis, respectively. M and P indicate that the dummy entered the money and
price equations, respectively.



TABLE A.5 - Dummy Variables Used in the Cointegration Analysis and Single Equation Estimations
Country   |Dummy Variable  Explanation                                           Dummy is included in...
Kenya      February-77       Controls for peak of M2 growth.                     MCI, M, and P eqn.
March-77         Controls for peak of M2 growth.                      M and P eqn.
February-80                                                           MCI
Controls for unidentified data outlier.
July-81          Dummy controls for drought.                          MCI
December-81      Controls for sharp increases in prices due both to the drought P eqn.
and to the upward adjustment in various administered prices.
March-85         Controls for first in a series of four adjustments in the central M and P eqn.
exchange rate.
March-88         Dummy controls for tight monetary policy; Central Bank    MCI and M eqn.
launches an aggressive treasury bond sale program.
May-91           Controls for unidentified data outlier.              P eqn.
March-92         Controls for unidentified data outlier.              P eqn.
June-92          Controls for unidentified data outlier.              P eqn.
February-93      Controls for unidentified data outlier.              P eqn.
March-93         Dummy controls for devaluation.                      MCI
April-93         Controls for sharp devaluation in the exchange rate as part of a M and P eqn.
macroeconomic policy adopted in April 93.
June-93          Controls for unidentified data outlier.              M and P eqn.
October-93                                                            MadPen
Controls for strong foreign exchange inflows around October M and P eqn.
1993; also, exchange rate was unified on October 17, 1993.
January-94       Controls for accelerating inflation due to surge in monetary   P eqn.
aggregates, drought, and price liberalization.
September-94     Controls for unidentified data outlier.              P eqn.
Malaysia   January-84        Dummy controls for withdrawal of subsidies for fuel.  MCI, ECI, M and P eqn.
February-84      Dummy controls for withdrawal of subsidies for fuel.  MCI, ECI, M and P eqn.
March-84         Dummy controls for withdrawal of subsidies for fuel.  MCI, ECI, M and P eqn.
Uruguay    November-82       Dummy controls for peso float in late November 1982.  MCI, WCI, M, and P eqn.
December-82      Dummy controls for sharp fall in peso value; as a result of the MCI, WCI, M, and P eqn.
float, the banking system experienced massive withdrawals of
foreign currency deposits.
December-87      Controls for debt-to-debt conversion scheme.         MCI, WCI, M, and P eqn.
January-88       Controls for debt-to-debt conversion scheme.         MCI, WCI, M, and P eqn.
November-89      Controls for unidentified data outlier.              MCI, WCI, M, and P eqn.
December-89      Controls for unidentified data outlier.              MCI, WCI, M, and P eqn.
January-90       Controls for unidentified data outlier.              MCI, WCI, M, and P eqn.
December-92      Controls for unidentified data outlier.              MCI, WCI, M, and P eqn.
Notatioi: MCI, WCI, and ECI indicate that the corresponding dummy was allowed to enter unrestrictedly in the money, labor,
and external sectors cointegration analysis, respectively. M and P indicate that the dummy entered the money and
price equations, respectively.



Policy Research Working Paper Series
Contact
Title                            Author                   Date              for paper
WPS2289 Exchange Rate Overvaluation and    Howard J. Shatz         February 2000      L. Tabada
and Trade Protection: Lessons    David G. Tarr                              36896
from Experience
WPS2290 Decentralization and Corruption:   Raymond Fisman          February 2000      E. Khine
Evidence across Countries        Roberta Gatti                              37471
WPS2291 Incentives for Pollution Control:  Jerome Foulon           February 2000      Y. D'Souza
Regulation and Public Disclosure  Paul Lanoie                               31449
Benoit Laplante
WPS2292 Dividing the Spoils: Pensions,     Ethan B. Kapstein       March 2000         P. Sader
Privatization, and Reform        Branko Milanovic                           33902
in Russia's Transition
WPS2293 Should Capital Flows be Regulated?   Roumeen Islam         March 2000         R. Islam
A Look at the Issues and Policies                                           32628
WPS2294 Reforming the Urban Water System    Mary M. Shirley        March 2000         P. Sintim-Aboagye
in Santiago, Chile               L. Colin Xu                                38526
Ana Maria Zuluaga
WPS2295 Resolving Bank Failures in Argentina: Augusto de la Torre  March 2000         M. De Loayza
Recent Developments and Issues                                              38902
WPS2296 An Ecological and Historical      Yujiro Hayami            March 2000         P. Kokila
Perspective on Agricultural                                                 33716
Development in Southeast Asia
WPS2297 Sources of Ethnic Inequality       Dominique van de Walle   March 2000        H. Sladovich
in Vietnam                       Dileni Gunewardena                         37698
WPS2298 Fiscal Deficits, Monetary Reform,    Nina Budina           March 2000         N. Budina
and Inflation Stabilization in Romania Sweder van Wijnbergen                82045
WPS2299 Household Savings in Transition    Cevdet Denizer          March 2000         A. Cubukcu
Economies                        Holger C. Wolf                             38449
Yvonne Ying
WPS2300 Single Mothers in Russia: Household  Michael Lokshin       March 2000         P. Sader
Strategies for Coping with Poverty    Kathleen Mullan Harris                33902
Barry Popkin
WPS2301 Identifying Welfare Effects from   Martin Ravallion        March 2000         P. Sader
Subjective Questions             Michael Lokshin                            33902



Policy Research Working Paper Series
Contact
Title                            Author                    Date               for paper
WPS2302 Why Liberalization Alone Has Not    Klaus Deininger         March 2000         M. Fernandez
Improved Agricultural Productivity    Pedro Olinto                           33766
in Zambia: The Role of Asset
Ownership and Working Capital
Constraints
WPS2303 Malaria and Growth                 F. Desmond McCarthy      March 2000         H. Sladovich
Holger Wolf                                37698
Yi Wu
WPS2304 Disinflation and the Supply Side   Pierre-Richard Agenor    March 2000         T. Loftus
Lodovico Pizzati                            36317