WPS4248
Monetary policy, structural break, and the monetary
transmission mechanism in Thailand*
Heiko Hesse
World Bank
Abstract
The paper studies monetary policy and the monetary transmission mechanism in Thailand
in light of the Asian Crisis in 1997. Existing studies that adopt structural VAR
approaches do not give a clear and agreed-upon view how monetary shocks are
transmitted to the Thai economy that is subject to structural breaks. This study explicitly
models a pre-crisis and post-crisis cointegrated VAR model. This analysis supports the
arguments of Corbett and Vines (1999) as well as Phongpaichit and Baker (2002) that the
trinity of open capital markets, pegged exchange rate regime and monetary policy
autonomy is inconsistent in the pre-crisis period. In contrast, the model points to an
effective monetary policy in the post-crisis period. Further, the author analyzes the
common driving trends of the model.
Keywords: Thailand; Monetary Policy; Monetary Transmission Mechanism; Asian
Financial Crisis; Cointegration; Common Trends
JEL classification: C32; E41; E58; F41
World Bank Policy Research Working Paper 4248, June 2007
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
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those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
*I am very grateful to Bent Nielsen for his help and suggestions during the process of the paper. I am also
thankful to Heino Bohn-Nielsen, Soren Johansen, Katerina Juselius and Anders Rahbek for providing
excellent support during a summer school on cointegrated VAR methods and comments on an earlier draft
at the University of Copenhagen. In addition, my thanks are due to Ronald Babula, Roy Bailey, Steve
Bond, Chris Bowdler, Julia Giese, David Hendry, Patrick Honohan, Takamitsu Kurita, Roland Meeks, John
Muellbauer, Jonas Stulz, David Vines, Ilknur Zer, as well as seminar participants at the U.S. International
Trade Commission, Bank of Thailand, Chulalongkorn and Thammasat University for helpful comments.
hhesse@worldbank.org.
1 Introduction
Thailand has undergone some rapid changes in its monetary policy framework in the last decade.
The collapse of its exchange rate, the baht, in 1997 led to the abandonment of the pegged exchange
rate regime and movement towards a monetary targeting regime implemented through the IMF
program. Following the end of the IMF program an ination targeting regime was introduced in
2000 where targeting of domestic money supply was replaced by an explicit ination objective.
Given these regime shifts and structural break of the Asian Crisis, existing work on the mon-
etary transmission mechanism in Thailand has been unsatisfactory. Many studies such as Pa-
trawimolpon et al (2001), Fung (2002), Disyatat and Vongsinsirikul (2003) and Hirunraengchok
(2004) build structural VAR models in order to analyse the responses of Thai economic variables
to monetary policy shocks. These studies often face econometric de...ciencies insofar as they do not
deal adequately with the structural break of the Asian Crisis, lack the property of a well-speci...ed
model or do not account for the nonstationarity of their macroeconomic data. The bottomline is
that many of such studies using the structural VAR approach to the Thai economy fail to render
a clear and agreed-upon view of how monetary policy shocks are transmitted to that economy.
In our study, we propose a di¤erent way of studying the monetary transmission mechanism in
Thailand based on the cointegrated VAR model. We ...rst estimate an unrestricted VAR model
and then determine the relevant long-run cointegrating relationships. Following an identi...cation
of the cointegrating vectors, we are able to identify the short-run structure of the cointegrated
VAR model and use the model to illuminate the patterns with which monetary policy shocks
are potentially a¤ecting key macroeconomic variables in the Thai economy. We make use of the
di¤erence between common stochastic trends that have permanent e¤ects on the system variables
and shocks to endogenous variables that are only transitory in character.
A major challenge to the analysis is that Thailand has been seriously hit by the Asian Crisis
in 1997. This will make the speci...cation of a model a very di˘ cult task because the relevant
variables of the model exhibit jump-like behaviour around the crisis. We anticipate that the
underlying stochastic process in the Thai economy has changed following the crisis. Similar to
Patrawimolpon et al (2001) and Fung (2002), we implement a subsample- based analysis, that, is,
a pre- crisis and post-crisis model but with the novel approach to rationalize and synthesize the
di¤erences between both models.
Our results for the pre-crisis model reveal that unlike many studies on Thailand, we cannot
recoup an interest rate policy function and that the central bank had di˘ culty controlling money.
These ...ndings support Corbett and Vines (1999) and Phongpaichit and Baker (2002) who state
that Thai monetary policy was immobilized in the period before the devaluation of the baht in 1997.
They put forward the Mundell-Fleming type argument that the trinity of open capital markets,
pegged exchange rate regime and monetary policy autonomy is inconsistent.
Regarding the Thai monetary transmission mechanism we ...nd that monetary shocks have
permanent positive e¤ects on ination. We do not obtain a Phillips curve relationship for the pre-
crisis model but ination appears to be a monetary phenomenon and caused by excessive liquidity
in the economy. In addition, interest rate shocks have consistently negative e¤ects on output. This
could be due to a possible ...nancial accelerator explanation (Bernanke et al, 1996).
2
For the post-crisis model, we observe that the cointegrating rank of the model increases. Mon-
etary policy becomes an e¤ective tool to steer the economy by virtue of the fact that the exchange
rate becomes exible, and we are able to obtain an interest rate policy function. We also ...nd
a type of money demand function in which money supply is related to the output gap and the
exchange rate which could proxy as an opportunity cost.
We organize the paper as follows: Section 2 gives a brief literature overview whereas section 3
provides an overview of the Thai economy as well as the data used in our study. In section 4, we
analyse the long-run relationships in a cointegrated VAR model using a pre-crisis and post-crisis
subsample. Section 5 provides evidence for the hypothesis that a full sample model that includes
the Asian Crisis is unstable. Following the long-run analysis we focus on the short-run dynamics
of the model by identifying the common stochastic driving forces (section 6). Finally, section 7
concludes with possible extensions to our work.
2 Literature Overview
In light of the regime shift and structural break of the Asian Crisis, there has been some recent
attempts by economists such as Hirunraengchok (2004), Disyatat and Vongsinsirikul (2003), Fung
(2002), Song (2002) and Patrawimolpon et al (2001) to model exogenous Thai monetary policy
shocks and the Thai monetary transmission mechanisms.
Speci...cally, Patrawimolpon et al (2001) adopt the methodology of Bernanke and Mihov (1998)
to the Thai economy.1 Based on the Choleski decomposition numerous alternative orderings are
adopted depending on whether the central bank targets the short term interest rate or the money
supply. Because of the structural break of the Asian crisis in July 1997, two di¤erent monthly
models are estimated, one for the pre-crisis period 1987:1 - 1997:6 and another for the post-crisis
economy in 1997:7 - 2000:4. Results of the impulse responses depend on the ordering of the
variables. For instance, in the pre-crisis model and the speci...c ordering where the central bank
targets the interest rate, we obtain the perverse e¤ect of a positive relationship between the money
supply (M1) and 14- days repurchase rate. If the central bank controls the money supply instead,
a negative relationship ensues for the pre-crisis period. The results of the post-crisis period are in
general unreliable due to the very short sample.
The methodology and reasoning of Patrawimolpon et al (2001) su¤er from some weaknesses.
The authors do not deal with the issue of nonstationarity. In other words, most variables are prob-
ably I(1) so we require either ...rst di¤erencing or cointegration techniques to ensure conventional
statistical inference. Also, the identi...cation of their monetary policy shocks based on the Choleski
decomposition is not unique, and results crucially depends on the ordering of the variables so
reliable inference from their impulse responses is very hard to obtain. Impulse responses of their
post-crisis models di¤er fundamentally from the pre-crisis model which provides evidence that the
structural break in 1997 shifted the underlying stochastic process of the economy.
1Bernanke and Mihov (1998) build a structural VAR model to investigate whether short term interest rates
decrease with monetary expansion, the so-called liquidity e¤ect, and whether there is no real e¤ect of monetary
policy in the long run (long run neutrality of money).
3
In a related vein, Fung (2002) investigates monetary policy shocks across seven East Asian
countries including Thailand. His pre-crisis model yields more sensible impulse responses than the
initial full sample model that included the Asian Crisis. The results of Patrawimolpon et al (2001)
and Fung (2002), by dividing their analysis into two subsamples, indicate that structural VAR
models that do not explicitly account for the Asian Crisis are inherently awed in their identi...cation
of monetary policy shocks and their policy implications from impulse response analysis.
A recent inuential study of the Thai monetary transmission mechanism was conducted by
Disyatat and Vongsinsirikul (2003).2 What di¤erentiates the study of Disyatat and Vongsinsirikul
(2003) from the other structural VAR models about Thailand is that they explicitly analyze the
various channels through which monetary policy changes are transmitted to the economy, that
is, the interest rate, balance sheet, bank lending, exchange rate and asset price channels.3 For
this purpose, they extend a basic monetary type model which includes GDP, consumer prices and
the 14-days repurchase rate with each of the relevant variables thought to constitute a particular
transmission channel. In most cases, the exogenize their unrestricted VAR with the exchange rate
in order to control for the e¤ects of the Asian Crisis. Their quarterly estimation sample is 1993:1-
2001:4. Their results are fairly robust to alternative causal orderings, and they test for parameter
stability using a di¤erent monthly model. Some critical remarks are directed to their short sample
of roughly 40 observations so potential degrees of freedom problems might occur. In addition, they
estimate their VAR equations in levels which raises the question of nonstationarity and unit root
issues. Lastly, inferences from robustness and speci...cation tests that are conducted on a di¤erent
monthly model in contrast to the employed quarterly model are invalid because sample periods and
the underlying stochastic process are potentially di¤erent. Chow test results are not independent
of the sample length.4
The bottomline of the structural VAR approach to the Thai economy is that the existing
studies do not give a clear and agreed-upon view how monetary policy shocks are transmitted
to the economy. Even though the identi...cation schemes vary, most studies face some common
de...ciencies. These are more from an econometric perspective and point to issues such as 'how
well-speci...ed is the statistical model?,' 'how to deal with structural breaks such as the Asian
Crisis?,''what about nonstationarity?,''how reasonable are arbitrary causal orderings if Choleski
decompositions are employed?' and ...nally, 'how sensible is it to assume speci...c forms of money
demand functions and macroeconomic relationships in the Thai economy a priori?'The last point
is very important because it directs the attention to the primary goal of this paper to conduct a
cointegration analysis of monetary policy and the monetary transmission mechanism in Thailand.
A few economic studies with an interest in the Thai money demand functions have explicitly
used the Johansen procedure to obtain long-run relationships between Thai economic variables.
2 They follow the approach by Bayoumi and Morsink (2001) who investigated the Japanese transmission mechanism
in a structural VAR model with the conventional Choleski decomposition. A related study who essentially follows
the same approach as Disyatat and Vongsinsirikul (2003) is Baqir (2002).
3 See Mishkin (1995) and Christiano et al (1999) for an overview of the monetary transmission channels and its
literature.
4 Alternative approaches that model monetary policy shocks as well as the Thai transmission mechanisms and
predominantly employ non-recursiveness assumptions are by Hirunraengchok (2004) and Song (2002). Whereas
Hirunraengchok (2004) estimates the model in ...rst di¤erences, Song (2002) assumes that the underlying VAR
process is covariance stationary.
4
Among them, Chowdhury (1997) ...nds in a cointegrating VAR that includes M1, output, prices,
exchange rate and a measure of the foreign interest rate, two cointegrating vectors. Further, Masih
and Masih (1996) reach similar results for a di¤erent sample period and frequency of observa-
tions. Hence, we can embed long-run relationships into the analysis of Thai monetary policy and
the monetary transmission mechanism without having to assume speci...c forms of money demand
functions and other macroeconomic relationships such as a monetary policy rule or ination ad-
justment equation. This approach is promising because once we have identi...ed the cointegrating
vectors, we can ...x them at their estimated values and treat them as predetermined stationary
variables in the ensuing identi...cation of the short run structure. In addition, the cointegration
methodology will allow us to explicitly account for the permanent and transitory driving forces
in the model by modelling the common stochastic trends in order to get some insights into the
Thai monetary transmission mechanism. Lastly, the de...ciencies of the above-mentioned struc-
tural VARs to the Thai economy o¤er room for improvement such as use of dummy variables,
sample period selection, adequate model speci...cation and invariance to the structural break by
subsampling.
In terms of our modelling strategy, similar cointegrated VAR studies have been undertaken by
Juselius (1996) and Beyer (1999) for the case of Germany. For example, Juselius (1996) obtains two
di¤erent monetary policy regimes for her sample period. In a related vein, Doornik et al (1996),
Pesaran and Smith (1998) and Juselius (2007) provide the underlying theoretical framework for
our study of the Thai monetary transmission mechanism.
3 Overview of the Thai Economy and Data
In general, the monetary policy framework in Thailand in the last twenty years can be charac-
terized by a basket approach in the pegged exchange rate regime from 1984- June 1997, a monetary
targeting regime from July 1997- May 2000 and an ination targeting regime until today.
Phongpaichit and Baker (2002) provide a thorough overview of the macroeconomic develop-
ments in Thailand over the last two decades. Following the abandonment of Thailand's import
substitution strategy in the mid-1980s, Thailand's macroeconomic framework was characterized
by a ...xed exchange rate regime as the nominal anchor and tight ...scal policy to pave the way for
the export boom.
Many international companies from East Asia set up production facilities in Thailand especially
in electrical and automotive industries. The export boom resulted in a tight labour market,
increased foreign competition and a negative current account in the early 1990s. The problem
was that at the same time, Thailand liberalized its ...nancial markets so that Thailand's inherent
problems were overshadowed insofar as its economy continued to grow, partly due to high volumes
of bank lending. During this period monetary policy was immobilized through the exchange rate
peg and the ...nancial liberalization.
A year before the crisis, Thailand su¤ered an export slowdown that resulted from decreasing
demand from Japan, accelerated competition from China and higher labor costs. Warr (1998)
provides a taxonomy of the boom and bust arguing that the investment boom in combination
5
with a consumption boom made the export sector very uncompetitive through cost increases, and
the exchange rate could not adjust. Furthermore, domestic ...nancial intermediaries increased their
exposure to foreign lending which arose from favorable interest rates abroad so this posed future
asset and liability problems to them.5
From another perspective, Kasajima and Lewis (2001) reason that the Thai nominal exchange
rate has been overvalued to the real e¤ective exchange rate, the latter which observed a real
depreciation for most of the early 1990s. In general, Thailand's current account de...cit reached a
historic high level in the second quarter of 1996. Note that we do not explicitly attempt to explain
the causes for the Thai currency and ...nancial crisis.6
Based on the negative external shocks, the Bank of Thailand decided to devalue the baht in
July 1997 which drastically increased the exposure of Thai companies to debt denominated in
foreign currencies. In that August, the Thai central bank ordered over 40 ...nancial companies
to suspend their operation. Rating agencies such as S&P reduced its sovereign risk rating for
Thailand to BBB, the minimum investment grade, in September. By November the same year,
both the ...nance and prime minister had resigned from their post. Alongside the turmoil on the
domestic ...nancial markets the authorities further deregulated the ...nancial sector by allowing
investors to increase their share ownership of existing ...nancial institutions (November, 1997) and
by dismantling currency controls (January, 1998). By the beginning of 1998, the rating of various
...nancial institutions reached junk status, and interest rates soared to historical high levels. In 2006,
Thailand still faces problems in terms of non-performing loans in a weakly functioning banking
system as well as high government debt levels.
The dataset consists of monthly observations from January 1989 up to December 2004. All
variables except the US price level are not seasonally adjusted. The following variables are mainly
used in the analysis:
Variable Details Source
RM1 Log real money M1 = LM1 LCPI IFS
LMPI Log manufacturing production index IFS
LCPI Log Consumer Price Index (Thailand) IFS
RP Repurchase rate, 14 days /1200 IFS
LEXCH Log nominal exchange rate (Baht/US Dollar) IFS
LCPIU Log Consumer Price Index (USA) LOU
PPP Log PPP relation = LCPI LCPIU LEXCH Own Calculations
LLENDING Log bank Lending IFS
Source: IMF International Financial Statistics (IFS), Labour O˘ ce in the USA (LOU)
Notes: L0 0 log, 'D'
Table 1: Overview of Variables
5Similarly, according to Warr and Vines (2003), Thailand's investment boom and bust mainly contributed to the
increased vulnerability of the economy.
6There is a huge literature on modelling the Asian currency and ...nancial crisis. See Krugman (1999), Masson
(1999), Agenor et al (1999), Irwin and Vines (2003) amongst others. Krugman (1999) attempts to explain the Asian
crisis from deteriorating ...rms'balance sheets and moral harzard problems whereas Masson (1999) postulates a story
in which contagion was the predominant factor. In contrast, Irwin and Vines (2003) combine various strands of
the so-called 'third generation models'into a coherent framework that involves investment collapse, moral hazard
problems, and multiple equilibria. Finally, Agenor at al (1999) provide a full conceptual, empirical and theoretical
account of the crisis.
6
0.03 0.02
2.0 RM 1 LMP I DLCP I RP
5.0 0.02
1.5 4.5 0.01 0.01
1.0 0.00
1990 2000 1990 2000 1990 2000 1990 2000
16
4.00 LEXCH -3.75PP P LLE NDING
3.75 -4.00
15
3.50 -4.25
3.25 -4.50 14
1990 2000 1990 2000 1990 2000
0.2 0.02
DRM1 0.1 DLMP I DDLCP I 0.005DRP
0.1
0.0 0.00 0.000
0.0
-0.1
-0.1 -0.02 -0.005
1990 2000 1990 2000 1990 2000 1990 2000
DLEXCH DP P P
0.2 0.2 0.05 DLLE NDING
0.0 0.0 0.00
-0.2 -0.2 -0.05
1990 2000 1990 2000 1990 2000
Figure 1: Log levels and ...rst di¤erences of the variables, 1989:01- 2004:12
Thailand faces some data problems because often monthly macroeconomic variables are only
available for a short sample period, usually starting at the end of the 1980s. For example, most
studies on monetary policy and the monetary transmission mechanism in Thailand have to rely on
quarterly data because measures of real GDP are not available on a monthly basis. For instance,
Disyatat and Vongsinsirikul (2003) only employ 40 observations in their study which makes their
results unreliable due to limited degrees of freedoms in their unrestricted VAR model estimation.
To avoid or minimize these problems, we chose to use the Thai manufacturing production index
(MPI) as a proxy for GDP, and we acknowledge the limitations in accuracy of this choice.7 The
14-days repurchase rate (RP) is a measure of the short-term interest rate.8
Figure 1 presents the (log) levels and ...rst di¤erences of the relevant system variables for the
full sample period.
We assume that long-run price homogeneity holds, that is, (DLM1 DLCPI) I(0) so that
money growth and ination are cointegrated and move together in the long-run. Given long-run
homogeneity, we know that (LM1 LCPI) I(1). Here, we follow the treatment of Juselius
(2007) who also assumes long-run price homogeneity. Including nominal money growth and prices
into the cointegrated VAR model could potentially complicate the analysis because of possible I(2)
7As a consistency check, we calculated the correlation coe˘ cient between quarterly GDP from its ...rst available
date and the analogue MPI. The correlation coe˘ cient from the ...rst quarter of 1993 to the fourth quarter of 2004
is around 0.94. Even though, the comparison does not cover the ...rst four years in our sample, it can serve as a
indication that the MPI is a good proxy for GDP uctuations.
8To make further analysis comparable with logarithmic monthly changes, RP is divided by 1200, to allow for a
monthly measure.
7
behavior. It is quite common in the cointegrated VAR literature with focus on monetary policy to
implement the nominal-to-real transformation.9
4 Cointegration Analysis
In the following, we estimate a pre-crisis and post-crisis model:
xt 1
4xt = 0 + 1 1 + t t
D + t (1)
t 4 xt + 0
where the vector of endogenous variables xt I(1); 0 = ( ; )0, 1 is the matrix of short-run
coe˘ cients, and the error terms t are assumed to be white noise with t N(0; ). The coe˘ cient
matrix tcontains the permanent as well as the seasonal dummies, and the trend is given by t.
We can think of the columns of as the number of cointegrating vectors, and is de...ned as
the adjustment vector, measuring how strong the cointegrating relationships feed back into each
individual equation in the above vector error correction form.
The pre-crisis model will contain the sample period from January 1989 up to the beginning of the
Asian Crisis in June 1997 whereas the post-crisis model comprises the period 1998:1- 2004:12. Note
that we omit six months within the crisis period in order to avoid the high degree of volatility of the
relevant endogenous variables. The variables included are real money, output, monthly ination, in-
terest rates and the nominal exchange rate, that is, x = fRM1;LMPI;DLCPI;RP;LEXCHg.10
The rationale for choosing some of the variables is as follows: As de...ned before, M1 denotes
narrow money which includes currency in circulation and demand deposits with the central bank
and private banks. By using real money RM1, we implicitly impose homogeneity of degree one
between nominal money and domestic prices. The Thai central bank operates the repurchase
market which is the predominant channel through which it conducts open market operations by
injecting or draining reserves to the banking system. Therefore, the 14-days repurchase rate RP is
a key interest rate that quali...es as a proxy of measuring the opportunity costs of holding money.
Various authors such as Disyatat and Vongsinsirikul (2003) and Fung (2002) also adopt RP as the
relevant interest rate. Lastly, we employ the monthly ination rate DLCPI instead of the yearly
rate because the volatility of the annual rate by far exceeds the monthly rate especially after the
Asian Crisis.
4.1 Pre-Crisis Model
The pre-crisis model contains some impulse dummies that we will outline in the following. An
impulse dummy takes the value one in the concerned month and the value zero before and after
the speci...ed period. An impulse dummy for October 1990 is included into the model for the
reason that in September 1990 Thailand was involved in a serious corruption scandal that saw the
dismissal of twenty ministers. This was just four months before a coup overthrew the government
and the prime minister was dismissed. We believe that it had some impact on ination.
9We also test whether the key ...ndings hold without the nominal-to-real transformation.
10 A restricted trend will be used in the estimation to avoid a quadratic trend if the trend was unrestricted. In
addition, an unrestricted constant will be utilized.
8
In addition, we incorporate an impulse dummy for June 1991. From a historical perspective
this dummy can be explained as follows: In 1991:4, Thailand was subject to some ...nancial market
liberalization. The government announced the loosening of foreign exchange rate controls, and it
introduced a value added tax system that took e¤ect in the following January 1992. The dummy
is able to account for the volatile movement of the interest rates during this period. We also recall
that our dataset is not seasonally adjusted so that we include 11 seasonal dummies into our model.
The estimation results based on a lag length of two indicate that the seasonal dummies are
highly signi...cant for the output variable LMPI in almost every month and for real money RM1 in
some months.11 Similarly, both impulse dummies are highly signi...cant.
In order to obtain a well-speci...ed model, it is essential to test for potential autocorrelation,
normality or ARCH e¤ects. These misspeci...cation tests do not show any problem. Furthermore, we
want to perform some parameter constancy tests in order to investigate the degree of uctuations of
the endogenous variables over the sample period. A base model with 50 observations is estimated
and the parameters are recursively estimated for the remaining sample period. There are no
signi...cant problems in these Chow tests.
The determination of the cointegrating rank r, that is, the number of relations that can be
classi...ed as equilibrium errors from steady-state, is a very delicate choice, and often requires not
only statistical judgment but also sound economic reasoning. Table 2 gives the result of the trace
test (see Johansen 1988, 1995) which indicates r = 2.12
p-r r Eig.Value Trace Frac95 P-Value
5 0 0:49 147:02 88:55 0:00
4 1 0:31 78:81 63:66 0:00
3 2 0:23 41:14 42:77 0:07
2 3 0:09 15:02 25:73 0:58
1 4 0:06 5:72 12:45 0:51
Table 2: Trace Test for Pre-Crisis Model
Auxiliary cointegration tests such as an inspection of the characteristic roots of the companion
matrix or the trace test without dummy variables con...rm our choice.13 Also, we could not ...nd
any I(2) trends in our model. Finally, some selected recursive tests were estimated in order to
examine the overall degree of parameter constancy in the rank-restricted (r = 2) cointegrated VAR
model.14 Evidence is generally insu˘ cient to reject the null hypothesis of parameter constancy
over the estimation period.
We continue our empirical analysis with some simple hypothesis testing on the adjustment
parameters and the pushing forces . The relevant results are reported in table 3 for r = 2.
11The optimal lag length of the unrestricted model based on likelihood ratio tests and the information criteria is
k = 1. Since the ...rst lag only allows for adjustment towards long-run equilibrium, and the second lag allows for
short-run e¤ects within the statistical model, we choose k = 2.
12The critical values of the trace test also account for the restricted trend because the choice of rank depends on
whether a trend is included in the cointegration space or not.
13The choice of the rank should be independent from the inclusion of permanent and seasonal dummies.
14For instance, the recursively calculated uctuation test of the eigenvalues measures the stability of the eigenvalues
for each of the two cointegrating relationships.
9
LR exclusion Stationarity Weak exogeneity
Statistic is 2(2) Statistic is 2(4) Statistic is 2(2)
RM1 8:49 (0:01) 33:55 (0:00) 6:12 (0:05)
LMPI 8:66 (0:01) 35:48 (0:00) 12:01 (0:00)
DLCPI 41:94 (0:00) 7:40 (0:12) 41:15 (0:00)
RP 0:70 (0:70) 22:65 (0:00) 2:74 (0:25)
LEXCH 7:66 (0:02) 31:15 (0:00) 0:61 (0:74)
Trend 10:87 (0:00)
Table 3: Tests of simple hypotheses on and for r = 2 (p-values)
Firstly, we conduct the null hypothesis that any given endogenous variable can be excluded
from the two steady state relationships. We cannot reject long-run exclusion for RP with a p-value
of 0.70 so the interest rate should not enter the cointegrating space. We have to be cautious because
from the matrix the interest rate is signi...cantly equilibrium correcting and also RP might be
slightly collinear with output and the exchange rate so potentially biasing the results. The test for
long-run exclusion further suggests that real money RM1, output LMPI, the monthly ination
rate DLCPI and the exchange rate LEXCH are potentially 'endogenous'variables so this is a
guidance in subsequently identifying the two cointegrating relationships.
Secondly, a test for stationarity without including the restricted trend is negative for all system
variables except the ination rate DLCPI with a p-value of 0.12. Thirdly, we conduct some weak
exogeneity tests. Weak exogeneity refers to the situation when a variable can inuence the steady-
state path of the remaining variables in the system but is completely invariant to the equilibrium
errors of these variables. In other words, there is no level feedback to the weakly exogenous
variables. The results of the tests indicate that the null hypothesis of weak exogeneity cannot be
rejected for RM1, RP and LEXCH. In addition, a joint weak exogeneity test is accepted with a
p-value of 0.05.
There is no level feedback to money, and shocks to money are permanent and not transitory.
It appears that the Bank of Thailand had di˘ culty in controlling money in the pre-crisis period.
Hence, we can observe scenarios where the money supply is determined by market forces, and the
central bank had only limited scope to control ination. Such a conclusion is tentative and subject
to our model validity.15
Besides real money, both the cumulated residuals of the interest rate and the exchange rate are
common stochastic trends in our system. The result for the exchange rate is not surprising because
it has been ...xed for the complete sample period. Hence, the exchange rate is not determined
endogenously within the system. By the fact that the interest rate is weakly exogenous, we are
unable to recoup any type of interest rate policy function such as a Taylor rule type. These results
are robust to replacing the 14-days repurchase rate with the alternative interest rate measure of
the money market rate.16
15For instance, our model is a small scale macroeconomic model so does not fully replicate real world phenomena.
The results are robust to using M2 instead of narrow money M1. But the ...ndings could not be invariant to a very
broad measure such as M4, even though Thai researchers predominantly adopt either the M1 or M2 measure. Also,
the fact that RM1 is weakly exogenous does not imply that nominal money is weakly exogenous and uncontrollable.
But we also estimate a model with nominal money instead and do ...nd that nominal money is weakly exogenous,
therefore providing some robustness for our above argumentation.
16Both the 14-days repurchase rate and the money market rate have a very high correlation of over 0.90 over the
10
The above ...ndings lend support to the conventional Mundell-Fleming model which postulates
that the trinity of open capital markets, pegged exchange rate regime and monetary policy au-
tonomy is inconsistent. Because monetary policy is primarily directed to maintaining the peg
between the Thai Baht and the US dollars, monetary policy e¤ects are potentially undermined
and immobilized.
For example, according to Corbett and Vines (1999), the inconsisteny of this trinity made the
Thai economy vulnerable to external shocks in the pre-crisis period. The ...nancial liberalization of
Thai capital markets in the late 1980s and early 1990s led to a inow of foreign capital. The Thai
central bank attempted to moderate the resulting economic boom by increasing interest rates in
1993-4. It argued that a pegged exchange rate regime and an open capital account were compatible
because of Thailand's export strength and its high reserves.
But the peg of the Thai Baht undermined the slow down of the economy because domestic
companies especially ...nancial intermediaries took advantage of the interest rate di¤erential between
domestic and foreign interest rates by increasing their foreign lending.17 More foreign lending, of
course, increased the companies'exposure to unhedged foreign debt and made the Thai economy
more vulnerable.
Phongpaichit and Baker (2002) share the same argument as Corbett and Vines (1999). They
reason that the central bank's monetary tightening in 1993-4 was counter-productive to cool down
the Thai economy because banks could just borrow abroad. In contrast, before the liberalization
of Thai ...nancial markets and a closed capital account, monetary policy was e¤ective in rais-
ing/decreasing interest rates to steer the economy through its business cycles.
Overall, our model suggests that the central bank had di˘ culty in controlling the money supply
because even though it managed to generate credit constraints in the Thai economy, Thai banks
could easily borrow on international ...nancial markets. Therefore, the creation of inside money
undermined the e¤ectiveness of monetary policy. The fact that both real money and the interest
rate are weakly exogenous, supports the above reasoning. In the following, we will identify the two
cointegrating relationships (table 4).
sample period. So the robustness of the results is not surprising.
17Through the exchange rate peg, domestic companies had in facto an implicit ...nancial guarantee of their foreign
lending. The ...nancial guarantee decreased the risk premium on investment which subsequently was feeding back
into the continuation of the investment boom. (see Corbett and Vines, 1999)
11
RM1 LMPI DLCPI RP LEXCH TREND
0:02 0:02 1 0 0 0
1
[ 3:27] [3:27]
0 1 0 5:18 0:54 0:0066
2
[2:72] [1:63] [ 46:41]
1 2
DRM1 0:52 0:18
[ 0:56] [ 1:66]
DLMPI 0:78 0:47
[ 0:78] [ 4:18]
DDLCPI 1:10 0:02
[ 8:87] [1:75]
DRP 0:05 0:003
[ 1:21] [ 0:59]
DLEXCH 0:06 0:05
[0:30] [2:43]
Table 4: An overidentifying long-run structure [t-values]
The ...rst cointegration relationship corresponds to an ination adjustment equation in which
ination is reacting to the inverse velocity of money. Formally,
DLCPI = 0:02 (RM1 LMPI) (2)
In other words, real money in excess of output causes inationary tendencies even though the
e¤ect is weak. Looking at the adjustment coe˘ cients , we observe that the ination rate is highly
reacting to the ination adjustment equation, as we would expect. Therefore, it appears that
the principal component to determine ination is inverse velocity, and this is consistent with a
monetary phenomenon interpretation. Conditional on the inverse velocity, there appears to be no
separate Phillips Curve e¤ect because uctuations in excess money are a good explanation for the
trajectory of ination. This result is analogous to Bhanthumnavin (2002) who does not ...nd any
Phillips Curve in Thailand for the pre-crisis period.
In the second long-run relationship, we obtain a reduced form IS-type equation of the following
form:
(LMPI 0:0066 trend) = 5:18 RP 0:54 LEXCH (3)
The left-hand side characterizes the output gap which has the conventional negative relationship
to the interest rate and is negatively reacting to the exchange rate. A possible explanation for the
negative interest rate e¤ect could be that the domestic interest rate proxies foreign interest rate
movements. The intuition is that if there was no independent monetary policy, the domestic rate
is tight to the foreign rate. Shocks to the foreign interest rates will inuence the domestic rate
which eventually feed back into output.
Another potential reason could be the ...nancial accelerator argument developed by Bernanke,
Gertler and Gilchrist (1996). In their theoretical framework, higher domestic interest rates will
put constraints on the balance sheets of manufacturing ...rms from deteriorating credit conditions.
Higher agency costs will lead ...nancial intermediaries to increase loan rates which subsequently
feeds back into lower output.
12
A possible explanation for the exchange rate e¤ect is that while exchange rate depreciation
might have improved the real exchange rate and promoted output, with the high levels of short-term
foreign debt, such an e¤ect is far outweighed by short-term foreign debt costs with depreciation.
The nominal exchange rate could thus proxy for the contractionary e¤ect on output of a huge
increase in foreign debt repayments. But we note from the t-value of the exchange rate that the
e¤ect is not very strong, and any exchange rate depreciation would have been rather minimal in
the pre-crisis period.
4.2 Post-Crisis Model
We recall that we omit the crisis months from 1997:7-1997:12. It precludes the high volatility
during the crisis months from distorting the new cointegrating relations. The exchange rate highly
depreciates, monthly ination jumps up due to immediate higher import costs, real money and
output sharply decline and the interest rate level soars to very high levels in the months following
the crisis. In such a scenario with the according political instability in Thailand, it is sensible to
leave out the crisis months for the post-crisis subsample analysis.
For the purpose of obtaining a well-speci...ed model, we aim at detecting potential outliers in
our sample and dummy them out. For instance, the volatility in the beginning of 1998 potentially
permeates upon the dynamics of the interest rate. Therefore, similar to the pre-crisis model, we
impose impulse dummy variables. Impulse dummies for March, June and July 1998 attempt to
capture very volatile movements in the interest rate. Furthermore, impulse dummies for December
1999, October 2000 and November 2003 primarily correspond to abrupt movements in real money.
For example, real money RM1 expanded in December 1999 at the same time when the Thai Baht
appreciated very strongly which had a positive e¤ect on Thai equities.
The ...ndings of the same unrestricted VAR model as in the pre-crisis period demonstrate that
the seasonal dummies succeed in capturing seasonal elements in both real money and output. As
anticipated, the impulse dummies for the interest rate uctuations and real money movements are
signi...cant. All the endogenous variables are highly equilibrium-correcting to themselves which
suggests that both money and interest rates appear to be endogenously determined within the
model in the post-crisis period unlike the pre-crisis.
Autocorrelation and ARCH e¤ects are no problems in our model speci...cation. Normality
cannot be rejected for all except the interest rate. This arises from its high volatility in the initial
post-crisis period. Interest rates reach historical heights in the early 1998s before subsequently
falling to very permanent low levels. Hence, a test of vector normality is rejected with a p-value
of 0.01. The post-crisis model is stable based on the Chow tests.
Table 5 gives the trace test for our well-speci...ed model which suggests r = 3.
13
p-r r Eig.Value Trace Frac95 P-Value
5 0 0:79 287:15 88:55 0:00
4 1 0:61 157:88 63:66 0:00
3 2 0:49 79:74 42:77 0:00
2 3 0:21 25:05 25:73 0:06
1 4 0:07 6:17 12:45 0:45
Table 5: Trace Test for Post-Crisis Model
It is reasonable to expect more than two cointegrating relationships in the post-crisis period
because the model should converge to a stable path if we assume that it has been out-of-equilibrium
prior to the crisis. Therefore, we immediately observe that the underlying stochastic process in the
Thai economy has altered following the Asian Crisis. We also could not ...nd any I(2) trends in our
model, and recursive estimation of the cointegrated VAR model provides evidence for parameter
stability.
No variable can be excluded from the cointegrating space, and stationarity tests indicate that
except the interest rate, no variable is stationary by itself and would form a cointegrating vector.
Note that the result for RP is slightly ambiguous because of the p-value under the null (0.10).
From the tests for weak exogeneity, the output proxy quali...es as a common stochastic trend
whereas the null hypothesis of weak exogeneity is strongly rejected for real money, ination rate
and the interest rate. Whereas output was determined endogenously in the pre-crisis model, it now
becomes signi...cantly weakly exogenous. One potential reason might be that output uctuations
cannot be explained by interest rates and the exchange rate because of the severity of the initial
post-crisis depression.
Conversely, both real money and the interest rate become highly endogenous after the Asian
crisis. The abandonment of the exchange rate peg contributed to the new results. The aforemen-
tioned impossible trinity of exchange rate peg, open capital markets and monetary policy control
has been broken. We also note also that the exchange rate was highly weakly exogenous in the
pre-crisis model but the fact that the exchange rate does not pass the null hypothesis of weak
exogeneity in the post-crisis period suggests an alteration of the exchange rate regime.
The preferred choice for the identi...cation of the three -vectors is presented in table 6. Note
that we already imposed weak exogeneity of output on the adjustment factors . The identi...cation
of the - and -vectors is accepted with a p-value of 0.86.
14
RM1 LMPI DLCPI RP LEXCH TREND
0:007 0:01 0 1 0 0
1
[ 2:58] [2:97]
0:16 0 1 0 0:05 0:0013
2
[ 18:98] [ 8:09] [18:03]
1 0:11 0 0 0:21 0:0077
3
[ 2:46] [5:67] [ 25:89]
1 2 3
DRM1 0:13 0:91 0:71
[ 0:13] [0:97] [ 4:96]
DLMPI 0 0 0
DDLCPI 0:39 1:00 0:15
[2:35] [ 6:45] [ 6:26]
DRP 0:13 0:003 0:0002
[ 13:70] [0:38] [0:17]
DLEXCH 0:47 3:84 0:41
[0:37] [3:26] [2:25]
Table 6: An overidentifying long-run structure [t-values]
Thus, the ...rst long-run relationship quali...es as a type of interest rate policy function in which
interest rates react positively to real money and negatively to output:
RP = 0:007 RM1 0:01 LMPI (4)
From the adjustment coe˘ cients, the interest rate signi...cantly responds to this long-run relation-
ship. An overidentifying test that inverse velocity holds is accepted with a p-value of 0.16. In such
a scenario, interest rates would be raised, if real money growth exceeds output growth. Secondly,
we obtain the following ination adjustment equation, in which there is inationary pressure in
the economy arising from higher real money balances and the trend-adjusted depreciation of the
exchange rate:
DLCPI = 0:16 RM1 + (0:05 LEXCH 0:0013 trend) (5)
We observe the usual exchange rate channel, in which depreciation increases domestic exports
and aggregate output that subsequently feeds back into inationary pressure. One possible reason
for including the trend on the exchange rate is that the nominal exchange rate was out of line
with fundamental factors. Therefore, we use a time trend that potentially picks up the exceptional
adjustment of the misaligned exchange rate after the crisis. In addition, besides the sensitivity of
the ination rate to this long-run relationship, the exchange rate reacts positively to an out-of-
equilibrium of the ination adjustment equation.
The third cointegrating relationship corresponds to a money demand function in which real
money responds positively to the output gap and negatively to the exchange rate:
RM1 = (0:11 LMPI 0:0077 trend) 0:21 LEXCH (6)
The e¤ect of the output gap on real money supply relates to the transaction motive for money
holdings whereas the exchange rate e¤ect could be characterized as the opportunity cost of holding
15
money.18 We could potentially rationalize this ...nding by arguing that the exchange rate is proxying
current expectation of future macroeconomic imbalances or uncertainties so that the central bank
uses monetary tools to correct for that. In other words, the exchange rate could be a leading
indicator for macroeconomic variables.
From the adjustment coe˘ cients, we observe that real money and ination react negatively
to the money demand equation whereas the exchange rate positively. The positive e¤ect of the
exchange rate is intuitive because excess money supply causes depreciation whereas the ination
e¤ect is counterintuitive. It could be possibly explained from the fact that Thai ination rates
have fallen during the Thai recession after the crisis when the central bank attempted to ease the
money supply in order stimulate the economy.
5 Full Sample Models
We focused substantial initial e¤orts on the full sample model and how to build a well-speci...ed
model in terms of misspeci...cation tests and especially parameter stability. Speci...cally, we es-
timated di¤erent versions of a partial model (Johansen, 1992). By conditioning the endogenous
variables on some exogenous variables we hope to capture the very large residuals associated with
the crisis. In other words, all the noise and volatility of the crisis period in 1997 is contained in
the exogenous variables. In a way, they substitute for employing numerous dummy variables.
We also estimated a linear broken trend model along the lines of Johansen et al (2000). The
intuition is that the exchange rate system in Thailand moved from a ...xed regime to a oating
regime so we might think of the crisis break as a linear trend that has been broken.
5.1 Model 1: Conditioning on the exchange rate
The intuition for exogenizing the exchange rate in the full sample model is that the exchange
rate forms the predominant volatility in the crisis period so conditioning on it helps to contain
statistical problems from that variable such as autocorrelation and normality issues. There are
some signi...cant problems with normality and autocorrelation in this partial model. Individually,
normality for the interest rate and output can be strongly rejected whereas their individual au-
tocorrelations seem ...ne. In addition, the interest rate does not pass the individual ARCH and
heteroscedasticity tests. Chow tests indicate that the interest rate is unstable around the crisis
period.
An interesting exercise for testing the stability of the model is to calculate the recursively
calculated components of the trace statistic. We know that the cointegrating rank changes from
the pre-crisis to the post-crisis model. Figure 2 shows that the partial model which conditions on
the exchange rate has no stable cointegrating rank. Inspecting the bottom graph which excludes
the short run uctuations we see that the ranks jumps from r = 2 to r = 3 shortly after the
devaluation of the baht.
18We could not ...nd any net interest rate spread between the interest rate and the exchange rate as a potential
building block for the money demand cointegrating relationship.
16
2.7
X(t)
2.4
2.1
1.8
1.5
1.2
0.9
0.6
0.3
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
The test statistic is scaled by the 5% critical value
2.8
R1(t)
2.4
2.0
1.6
1.2
0.8
0.4
0.0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
H(0)| H(4) H(1)| H(4) H(2)| H(4) H(3)| H(4)
WARNING: THE CRITICAL VALUES ARE INVALID!
Figure 2: Recursively estimated trace test based on 75 initial observations
In an alternative partial model, we used as a second exogenous variable Thai bank lending
with the underlying reason that Thai bank lending growth could qualify as the variable that might
explain the structural shift during the crisis period and measure the jump from one equilibrium
pre-crisis to the second equilibrium post-crisis. In other words, bank lending could be thought as
a proxy for overinvestment in the pre-crisis period, capturing the importance of the investment
boom and bust argument by Warr and Vines (2003). This reasoning is plausible since most Thai
investment relied on bank-based funding rather than capital markets. But this model is not stable
as well.19
As discussed before, Thailand su¤ered a serious current account de...cit in the pre-crisis period
which was often seen as a contributing factor for the worsening economy before the Asian Crisis.
We also attempted to include a current account variable as an exogenous variable into the model in
various combinations with the exchange rate and banking lending but no stable and well-speci...ed
VAR model could be obtained. Finally, partial models with a PPP relation as one of the exogenous
variables were not successful as well.
5.2 Model 2: Broken linear trend model
Johansen et al (2000) analyze the cointegrated VAR model subject to structural breaks in
the trend. The break points are known, and the trend is linear but piecewise. In general, a
cointegrated VAR model for each subsample is estimated in which the parameters are the same
for all subsamples except the deterministic trend component which could potentially be di¤erent.
Tests for misspeci...cation indicate the strong presence of autocorrelation in the exchange rate.
Also, 1-step and breakpoint Chow tests in ...gure 3 provide evidence that the exchange rate faces
a structural break in the broken linear trend model. The problem with this model is that we
endogenize the exchange rate so the model su¤ers from the noise and volatility of that variable
around the crisis period.
19Instead of nominal banking lending which is a stock variable we also used the ination adjusted banking lending
growth rate as an exogenous variable but the full sample model remained unstable.
17
1.5 1.0
1up RM1 1% 1.0 1up LMPI 1% 1up DLCP I 1%
1.0
0.5
0.5 0.5
0.0 0.0 0.0
2000 2005 2000 2005 2000 2005
1.5
1up RP 1% 30 1up LEXCH 1% 15 1up CHOWs 1%
1.0 20 10
0.5 10 5
0.0 0
2000 2005 2000 2005 2000 2005
1.0 1.0 1.0
Ndn RM1 1% Ndn LMP I 1% Ndn DLCP I 1%
0.5 0.5 0.5
2000 2005 2000 2005 2000 2005
1.0
Ndn RP 1% Ndn LEXCH 1% Ndn CHOWs 1%
20 2
0.5 10 1
2000 2005 2000 2005 2000 2005
Figure 3: 1-step and breakpoint Chow tests in broken linear trend model
To conclude, we did not manage to ...t a well-speci...ed cointegrated VAR model through the
crisis period. The bottomline is that our subsample technique is superior in investigating the
dynamics in the VAR model and giving some information about how some channels of the Thai
monetary transmission mechanism work. The ...ndings support our argument in section 2 that
many structural VAR models about Thailand are misspeci...ed especially when ...tting a model
through the crisis. Among them are studies by Hirunraengchok (2004), Song (2002) and Disyatat
and Vongsinsirikul (2003). In contrast, it supports the underlying reasoning of Patrawimolpon et
al (2001) and Fung (2002) of a subsample-based analysis.
6 Short- Run Dynamics: Identi...cation of common driving trends
In this section we will informally describe the moving average (MA) representation of the coin-
tegrated VAR model because it helps us to understand the common stochastic trends (permanent
shocks) and the transitory shocks in the model (see Juselius, 2004, for a formal analysis). Speci...-
cally, we impose restrictions on the common trends. Our primary interest is how permanent shocks
to the system permeate on the system variables especially the endogenous variables of interest. This
exercise gives us some indication of the monetary transmission mechanism in Thailand insofar as
we are able to gauge the e¤ects of monetary policy shocks to the economy.
In general, in the Granger-Johansen representation, the C matrix (see Juselius, 2004) contains
important information how variables are a¤ected by the stochastic driving trends in the system.
For example, the columns of the C matrix indicate how the cumulated shocks to any individual
18
VAR equations feed into each of the endogenous variables. In contrast, a row wise inspection
provides information of the weights of the cumulated shocks for each variable.
Table 7 reports the C matrix based on both the restricted and for the pre-crisis model,
that is, we have imposed our two cointegrating relationships and the weak exogeneity of RM1, RP
and LEXCH. Thus, the columns of ^"LMPI and ^"DLCPI have been set to zero.
^"RM1 ^"LMPI ^"DLCPI ^"RP ^"LEXCH trend
RM1 0:88 0 0 0:62 0:36 0:0059
[10:89] [ 0:23] [ 0:71]
LMPI 0:01 0 0 1:26 1:00 0:0064
[ 0:03] [ 2:18] [ 9:20]
DLCPI 0:01 0 0 0:01 0:01 0:00
[10:83] [0:23] [1:25]
RP 0:01 0 0 1:00 0:02 0:00
[ 1:29] [7:83] [ 0:64]
LEXCH 0:01 0 0 0:46 1:11 0:0003
[0:45] [ 0:38] [8:38]
Table 7: The pre-crisis MA impact matrix, restricted and
Restricting the C matrix, we observe from the t-values that shocks to money have not any
permanent e¤ects on output because the null hypothesis of a zero e¤ect cannot be rejected. Fur-
thermore, there is strong indication that positive shocks to money increase the ination rate and
money itself permanently which suggest that money shocks are an important pushing force of our
cointegrated VAR model. Also from an inspection of the two remaining weakly exogenous variables
RP and LEXCH, we detect the expected negative e¤ect of interest rate shocks to output and the
negative e¤ect of exchange rate shocks to output as discussed before.
Analyzing the C matrix row-wise reveals that ...rstly, output is predominantly inuenced by
shocks to the interest rate and the exchange rate, and that secondly, the ination rate's predominant
inuence is empirical shocks to money.
Overall, the above ...ndings are consistent with the results from section 4. Speci...cally, the
three weakly exogenous variables RM1, RP and LEXCH are the signi...cant pushing forces in our
model, and in contrast, the forces that pull back the system to equilibrium are represented by the
equilibrium adjustment mechanism in the equations for output and the ination rate.
For the post-crisis model, we observe that positive shocks to output have permanent e¤ects
on money, own output and ination. The permanent e¤ect on money suggests that money ac-
commodates the increase in output, potentially through higher transaction demand for real money
holdings. Note that this result is consistent with the money demand cointegrating relationship in
which we observed a positive long-run e¤ect from output to money. Further, the positive relation
between output and ination is economically intuitive, that is, shocks to output put pressure on
prices.
In addition, there is evidence that shocks to the cumulated residuals of money and the interest
rate are only transitory in character. This complies with our previous ...ndings in section 4 where
we found signi...cant adjustment coe˘ cients in for both variables. Also note that there is some
weak evidence that ination shocks at least inuence two system variables permanently: Money
and the exchange rate. The negative e¤ect on money potentially implies that monetary policy is
counteracting the e¤ects of positive ination shocks.
19
We recall that we rejected weak exogeneity for the exchange rate in section 4 but we argued
from its insigni...cant adjustment coe˘ cients that the exchange rate will not be a normalizing
variable in the subsequent identi...cation of the . Our line of argumentation is consistent insofar
because shocks to the exchange rate have permanent e¤ects on money, the ination rate and the
exchange rate itself. The e¤ect on ination is positive whereas exchange rate shocks contract
money holdings. In other words, the result might suggest that the central bank would counteract
inationary tendencies by contracting money following a depreciation.
7 Conclusion and Possible Extensions
We attempted to analyze monetary policy and the monetary transmission mechanism in the
Thai economy based on a well-speci...ed cointegrated VAR approach. Our ...ndings reveal that any
full sample modeling strategy that incorporates the Asian Crisis will be misspeci...ed, and policy
inferences from such a model are potentially invalid. Our subsample-based modeling strategy is
able to account for the e¤ects of the structural break and pinpoint the fundamental di¤erences in
both the pre-crisis and post-crisis model.
Speci...cally, we ...nd that Thai monetary policy was potentially immobilized in the pre-crisis
period by virtue of the pegged exchange rate regime and the ...nancial liberalization of the Thai
economy. Many existing studies that investigate the monetary transmission mechanisms in Thai-
land do not take account of this fact and make assumptions of the monetary policy stance of the
Thai central bank in this period. For example, Patrawimolpon et al (2001) assume that the central
bank either targets money supply or the interest rate in their pre-crisis model. Such assumptions
are potentially invalid insofar because they are at odds with the typical Mundell-Fleming type ar-
gument of the inconsistency of the trinity independent monetary policy, ...xed exchange rate regime
and open capital account. Analogous to the above argumentation, we do not obtain an interest
rate policy function.
Our two cointegrating vectors in the pre-crisis model correspond to an ination adjustment
equation and IS-type equation. Similarly to Bhanthumnavin (2002), we cannot obtain a Phillips
Curve relationship. Ination seems foremost a monetary phenomenon and is positively related to
the inverse velocity of money.
Our results for the pre-crisis monetary transmission mechanism are consistent with the iden-
ti...cation of the common stochastic trends. In details, money shocks have permanent e¤ects on
ination but only transitory e¤ects on output. The transitory response of output supports the
reasoning of Corbett and Vines (1999) and Phongpaichit and Baker (2002) that the monetary
tightening of the Thai central bank in 1993-4 to cool down the economy was ine¤ective because
...nancial intermediaries were able to borrow abroad at cheaper interest rates. Also, there is no
channel from monetary shocks to the exchange rate which is economically intuitive. Shocks to the
interest rates signi...cantly inuence output which we attributed to a possible ...nancial accelerator
argument (Bernanke et al, 1996) or the fact that domestic interest rates move with foreign interest
rates which were not included in the model.
In the post-crisis model, we do ...nd a money demand equation in which money supply is
20
determined by the output gap and the exchange rate which could proxy as the opportunity cost
of holding money. One potential explanation could be that the exchange rate is proxying current
expectation of future macroeconomic imbalances or uncertainties so that the central bank uses
monetary tools to correct for that. Of course, the type of money demand is subject to the included
system variables. Further, we obtain an interest rate policy and ination adjustment equation.
In general, we ...nd it very di˘ cult to explain the short run behaviour of the exchange rate by
fundamental factors which complies with the arguments of Meese and Rogo¤ (1983).
What is the bottomline of the paper? We still do not know the full monetary transmission
mechanism of money shocks to the economy, although we could disentangle various components.
The question is 'What is the black box?' to borrow the terminology of Bernanke and Gertler
(1995). To answer the questions, we could extend the model with further macroeconomic variables
such as investment, stock market and bank lending data in order to obtain a full picture how
monetary policy shocks are transmitted to the economy through the asset price and credit channels.
For instance, Disyatat and Vongsinsirikul (2003) add these additional variables individually to a
small (three variable) VAR model. Their approach is problematic because they do not deal with
nonstationarity so any ...ndings will be potentially biased if equations are estimated in levels.
We anticipate that it will be a di˘ cult task to incorporate these variables into the cointegrated
VAR framework. The structural break of the Asian Crisis complicates such an extended study.
Also, we might lose tractability of the model if the dimension rapidly increases, an argument
which Juselius (2007) elaborates on. This is surely a limitation of our VAR approach because the
economy is an interrelated dynamic system in which many factors inuence the key macroeconomic
indicators. Our initial aim has been modest because data issues and the Asian Crisis made it very
di˘ cult to decompose the short- run dynamics of the system variables.
From a policy viewpoint, we see scope for further improvements of the cointegrated VAR
modelling approach in order to draw sensible statistical inferences. For instance, the literature
on forecasting in a nonstationary world (Clements and Hendry, 1999) becomes relevant. But a
forecasting exercise would be rather limited because forecasts of the pre-crisis model will su¤er
from the structural break in 1997.
Another interesting forecasting exercise would be to estimate a full sample cointegrated VAR
model and analyze the factors that signi...cantly contribute to the forecast failure of the Asian
Crisis. We potentially could adopt the methodology of Clements and Hendry (1999) who provide
a taxonomy of forecast errors in varying macroeconomic settings. According to their framework,
deterministic shifts are usually the primary determinant of forecast failure. But we know from the
estimation of the broken linear trend model by Johansen et al (2000) that a deterministic trend is
not able to potentially overcome the structural break during the Asian Crisis. Therefore, possible
di¤erent explanations for the forecast failure would provide a valuable addition to our reasoning
in section 5 to account for the di¤erences in the pre- and post-crisis period.
An issue which is often neglected in related studies is the quality of the data. We argued before
that Thailand faces some data problems in terms of the availability of monthly frequencies for
GDP. The manufacturing production index (MPI) that we adopt can only imperfectly qualify for
measuring the movements of GDP.
21
Finally, our results could be potentially adopted to di¤erent country experiences in South East
Asia that had ...xed exchange rate regimes and were a¤ected by the Asian Crisis. Supposedly, many
South East Asian countries liberalized their ...nancial markets in the early 1990s, and therefore
there might be cross-country similarities between the monetary transmission mechanisms and the
degree of e¤ectiveness of monetary policy. For instance, Fung (2002) attempted to explain these
in a structural VAR setting. Our cointegrated VAR methodology could provide an alternative to
answer such questions.
22
References
Agenor, P.R., M. Miller, D. Vines and A. Weber (1999) ed. The Asian Financial Crisis: Causes,
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Baqir, R. (2002) "The channels of monetary policy transmission in Thailand,"in Thailand: Selected
Issues and Statistical Appendix, Country Report No. 02/195, IMF
Bayoumi, T. and Morsink, J.(2001) "A Peek Inside the Black Box: The Monetary Transmission
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