WPS5065
Policy Research Working Paper 5065
Exchange Rate and Output Fluctuations
in the Small Open Economy of Mauritius
Fabiano Bastos
Jose Angelo Divino
The World Bank
Africa Region
Poverty Reduction and Economic Management Department
September 2009
Policy Research Working Paper 5065
Abstract
The authors estimate a VAR and compute generalized analysis of the inflation output trade-off, but evidence
impulse response to analyze the joint dynamics of four points to a weak relationship in the short run as well.
key macroeconomic variables in the small open economy These findings are used to shed some light into the policy
of Mauritius. Results suggest that nominal exchange rate response to the current worldwide economic slowdown
and interest rate have limited ability to impact output affecting Mauritius.
growth over the medium-run. Large error bands hinder
This paper--a product of the Poverty Reduction and Economic Management Department, Africa Region--is part of a
larger effort in the department to monitor macroeconomic developments in Mauritius and assess policy options. Policy
Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at
fbastos@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Exchange Rate and Output Fluctuations in the
Small Open Economy of Mauritius
Fabiano Bastos Jose Angelo Divino
World Bank Graduate Program in Economics
Washington DC Catholic University of Brasilia, Brazil.
Email: fbastos@worldbank.org Email: jangelo@pos.ucb.br
1. Introduction
Mauritius is a development success story. In late 1960's, its GDP per capita was a bit less than
US$ 300. Fifty years later, the GDP per capita reached more than US$ 6,000. While there is
debate around the explanations for such a success, the crucial role played by trade preferences in
Sugar and Textile is widely acknowledged Roy and Subramanian (2001). As preferential
market access dwindled over the last five years, Mauritius had to deepen exploitation of other
growth drivers. To that end, the last three years witnessed an acceleration of reforms aiming at
transforming the economy in a high-value added, globally competitive and trade integrated
services hub. In the process of doing so, solid macroeconomic management was reinforced as a
key development pillar. A managed float regime for the exchange rate, an improving institutional
framework for monetary policy and greater fiscal responsibility were all understood as necessary
ingredients. At the same time, a number of longer-term initiatives dealing with human capital,
infrastructure, public sector effectiveness and regulatory environment are being undertaken.
In this context, the world economic crisis initiated in late 2008 has caught Mauritius at a
crossroads. Key export markets for textile and tourism are in recession, FDI inflows have slowed
down and the banking sector is also vulnerable to developments in international capital markets.
While the path towards solid economic performance still requires attention to various structural
bottlenecks, avoiding growth decelerations today is key Arbache and Page (2007). A mix of
macroeconomic and microeconomic policies is being pursued by the government. On the macro
front, policies have been characterized by a temporary fiscal stimulus, loosening of the monetary
policy, and a nominal exchange rate largely determined by market forces. In this context, calls
for more aggressive reduction in interest rates are common, aiming not only at a lower cost of
credit but mainly at an aspired faster depreciation of the exchange rate to boost competitiveness
in the exporting sector.
The concept of competitiveness can be used to motivate different and at times even conflicting
policy actions. Good physical infrastructure, appropriate human capital base, technological
absorption and a regulatory environment conducive to private sector development are all
necessary for the fundamentals of competitiveness to exist. Unfortunately, addressing these
complex issues is not simple and often takes a long time to bear fruit. In the context of the
current economic slowdown and the pressing need to sustain employment and growth, a push for
greater nominal exchange rate depreciation in Mauritius emerges as a quick solution to boost
competitiveness of the export sector and stimulate growth. A few observations are important:
(i) With faltering external demand, domestic competitiveness (whatever one
understands by it) has limited scope to be translated into strong export
performance. Mauritius exports are non-diversified and key markets in Europe for
tourism and textiles are undergoing severe economic downturn.
(ii) Real, not nominal exchange rates, is what really affects competitiveness.
However, this is not a policy variable. Instead, it is a price that will adjust in
response to internal and external equilibrium conditions and which is linked to the
rest of the world through the international price level. A global recessionary
environment affecting some of the Mauritius key trading partners complicate any
prospects for real exchange rate devaluation and competitiveness gains that could
come from it. First, currencies of developed economies are under depreciating
pressure due to weak fundamentals. Second, international price levels may fall
faster than domestic levels due to a relatively worse economic environment
abroad. Mechanically, both factors contribute to the appreciation of the real
exchange rate in Mauritius, and mitigate depreciating forces coming from
subdued export performance and worsening prospects for capital accounts.
(iii) It is not clear that lower interest rates is a very effective mechanism to achieve
nominal depreciation of the exchange rate in Mauritius - in particular, the
portfolio channel of the capital account is not particularly strong vis a vis its other
components. Additionally, direct intervention in the nominal exchange rate
market to push the currency to levels that are weaker than those determined by the
market would eventually be (at least partly) neutralized by higher domestic prices
(import inflation). So real exchange rates, and thus competitiveness, would not be
affected necessarily.
It could be argued particularly against points (ii) and (iii) that adjustments in the real
exchange rate via price level changes are not immediate. Hence, nominal depreciation of the
exchange rate would still generate a positive short-run impact. Given the current circumstances,
one could claim that even a temporary boost of exchange rate competitiveness would be
instrumental for the economy to withstand the bad times and perhaps even avoid policy reform
stagnation/reversals that could be costly for competitiveness tomorrow. So a relevant policy
question to ask is how output growth would respond to nominal exchange shocks over the space
of a year or so.
We run a reduced-form VAR to shed some light into this question. Based on the observed
dynamics of four key macroeconomic variables since 2001, the exercise allows one to trace the
impact on output growth in response to nominal exchange rate shocks accounting for feedback
effects in inflation and interest rates. It is important to highlight that the evidence presented here
is not definitive by any means. First of all, this is a small-scale reduced-form model. Secondly,
one could claim that the Mauritius economy is undergoing structural changes that are particularly
hard to capture in an econometric exercise with the sample size used. Hence, the evidence we
present should be interpreted with the due caution.
The paper is organized as follows. Next section discusses the VAR and generalized impulse
response estimation. The third section presents the data set, applies unit root tests, computes and
analyzes the impulse response. The fourth section is dedicated to concluding remarks.
2. The VAR approach
Since the pioneer work of Sims (1980), the VAR framework has been extensively used to
analyze monetary policy issues. Bernanke and Blinder (1992), Bernanke and Mihov (1998), Kim
(2001), and Bagliano and Favero (1999) are a few examples of application to analyze different
aspects of monetary policy. The VAR approach, however, is often criticized for heavily
depending on ad hoc identification assumption. In this paper, we deal with this criticism by
computing generalized impulse responses, which do not rely on any a priori identification
assumption. The reduced-form specification of the VAR is as follows:
(1)
where , with , is a vector of jointly determined dependent
variables, to be defined in the next section, is a vector of deterministic and
exogenous variables, , and are and coefficient matrices. The
residuals are assumed to be Gaussian . In the empirical analysis, the vector will
be augmented to include dummy variables for structural breaks in the time series. To select the
optimal truncation lag, p, one can use the information criteria of Akaike and Schwartz.
Under the assumption that each time series in (1) is covariance-stationary, the VAR can be
written as an infinite vector moving average (VMA) representation:
(2)
where and are and coefficient matrices with . Note that when there are
only deterministic terms in , there will be no lagged variables in the augmented term.
The VMA representation in (2) is used to compute impulse-response functions. A key issue,
however, refers to the identification of the structural residuals, which are functions of the
reduced form ones. A standard practice in the empirical literature is to adopt a Cholesky
triangular decomposition, where the variables are ordered according to an assumed decreasing
exogeneity ranking. Under some circumstances, this identification strategy is equivalent to the
one proposed by Sims (1980). In general, the results are quite sensible to changes in the ad hoc
order of the variables. To overcome this limitation, Pesaran and Shin (1998) proposed the
generalized impulse responses (GIR), which are invariant to reorder of the variables in the VAR.
Given some assumptions on , they show that GIR are unique and fully take account of
correlations among different shocks.
The standard-deviation scaled GIR of the effect of a shock in the ith equation, i 1, 2, 3, 4 , at
time t on Yt n , n periods ahead, is represented by:
GIRi ( n ) ii 1 / 2 n i
(3)
where ii are the main diagonal elements of E t t' for all t, with ji , j , i 1,2,3,4 a
positive definite matrix, n is a coefficient matrix from (2), and i is an 41 selection vector
with unity as its ith element and zeros elsewhere. Cleary, the GIR in (3) does not depend on any
lower triangular matrix, which defines the variables ordering in a Cholesky stile residual
decomposition.
Kim (2009) argues against using GIR because they employ a set of extreme identifying
assumptions. Economic inference based on GIR would be misleading, unless the covariance
matrix is diagonal. In order to address those issues, we use a standard Cholesky identification
scheme to compute orthogonalized impulse response and show that the main results also hold
under a conventional ordering of the variables.
3. Results
3.1 Data
The model has five macroeconomic variables: (i) real output growth, (ii) inflation, (iii) interest
rate (iv) nominal exchange rate, and (v) oil price. The first four are treated as endogenous while
the last one is exogenous in the model. Real output growth ( is obtained directly from the
Central Statistics Office and calculated on the basis of the GDP deflator. Inflation ( measures
yearly variation in the Consumer Price Index and is also obtained from the Central Statistics
Office. The interest rate ( ) used is the weighted average interbank rate published by the Bank of
Mauritius, which is not a pure policy variable and should be understood as capturing the liquidity
stance of the economy. Nominal exchange rate ( ) is obtained from the same source. We also
use oil prices ( ) as an exogenous variable in the VAR. The data is quarterly and the sample
runs from 2001Q1 to 2008Q4. Figure 1 display the time series.
Figure 1 Time series
Exchange rate Output growth
34 10
33
8
32
6
31
30 4
29
2
28
0
27
26 -2
2001 2002 2003 2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 2006 2007 2008
Inflation Interest rate
14 10
12
8
10
6
8
4
6
2
4
2 0
2001 2002 2003 2004 2005 2006 2007 2008 2001 2002 2003 2004 2005 2006 2007 2008
3.2 Unit root tests
The assumption of stationarity must be tested in the data. Traditional tests, however, based on
Dickey-Fuller and Phillips-Perron, are criticized for suffering from lower power and size
distortions. As stressed by Ng and Perron (2001), statistical power is lower for highly persistent
time series, while size problems are determined by the presence of a strong negative MA
component in the series representation. Improvements in the test procedure have been proposed
by Perron and Ng (1996), Elliott, Rothenberg and Stock (1996), and Ng and Perron (2001).
Essentially, the modifications evolve the combined use of GLS detrended data and the modified
Akaike information criterion to choose the optimal truncation lag for the augmented term of the
test equation. Asymptotic critical values for both tests, labeled MADFGLS and MPPGLS, are
reported in Ng and Perron (2001).
The presence of structural breaks in the time series might severely bias the previous tests. To
account for such breaks, it is applied tests proposed by Perron (1997) and Lee and Strazicich
(2003), which endogenously select the time of the breaks. Perron (1997) proposes a test that
allows for a change in both intercept and slope at time Tb , which is made perfectly correlated
with the data. A potential problem with the Perron (1997) test is that it assumes no structural
break under the null of unit root. Lee and Strazicich (2001) show that this assumption can result
in spurious rejections. The two-break minimum LM unit root test, due to Lee and Strazicich
(2003), is unaffected by whether or not there is a break under the null hypothesis. The results are
reported in Table 1.
Table 1 Unit root tests
MADFGLS MPPGLS
Z={1} Z={1, t} lags Z={1} Z={1, t} lags
gt -0.280 -3.391* 5; 1 -0.678 -2.607 5; 1
t -0.626 -1.379 4; 4 -0.811 -1.142 4; 4
it -1.282 -1.472 0; 0 -1.192 -1.254 0; 0
et -1.770 -2.029 0; 0 -1.539 -1.796 0; 0
opt -0.814 -2.472 3; 0 -1.123 -1.940 3; 0
5% cv -1.95 -3.19 -1.98 -2.91
Perron (1997)
Model Statistic Lags Break 5% cv
gt IO2 -250.14** 12 2004:03 -5.59
t IO2 -28.525** 12 2007:03 -5.59
it IO1 -4.381 10 2004:03 -5.23
et IO2 -223.96** 12 2007:04 -5.59
opt AO -5.413* 1 2004:02 -4.83
Lee e Strazicich (2003)
Model Statistic Lags Break 1 Break 2 5% cv
gt 2 -8.479** 3 2004:3 2007:3 -5.29
t 2 -8.024** 3 2002:3 2006:1 -5.29
it 2 -5.483* 4 2003:3 2006:1 -5.29
et 2 -5.860* 2 2003:3 2007:2 -5.29
opt 2 -10.099** 1 2004:4 2007:3 -5.29
Note: * and ** mean that the null of unit root is rejected at the 5 and 1% significance level, respectively. cv stands for critical value.
The first panel of Table 1 shows that, except for output growth under the MADFGLS test with
constant and trend, the null of unit root is not rejected at the standard 5% significance level
according to both tests. This result, however, might be due to the presence of structural breaks.
The Perron (1997) test reaches a different conclusion. The results, reported in the second panel
of Table 1, indicate that all time series but nominal interest rate are stationary once a structural
break is appropriately modeled. The time of the break is endogenously chosen by the maximum
value of the t-statistic on the coefficient of the shift dummy variable. The adverse result for the
nominal interest rate might be because it has more than one break in the period, as suggested by
Figure 1.
The last panel of Table 1 reports results for the Lee and Strazicich (2003) two-break LM unit
root test. It indicates that nominal interest rate should be joined to the other time series as a
stationary variable. The times of the endogenously chosen two-breaks coincide with the changes
observed in Figure 1. Thus, the results of Table 1 suggest that output growth, inflation rate,
nominal interest rate, exchange rate, and oil price are stationary in the period under
consideration.
3.3 Impulse Response and Policy Implications
To assess the impact of nominal exchange rate shocks over output growth taking into
consideration inflation and interest rate dynamics, we obtain impulse response functions from the
estimated VAR above. We use Pesaran's approach of generalized impulse as a decomposition
method. The truncation lag was set to 4, according to the information criteria of Akaike and
Schwartz. Accumulated responses to one standard-deviation innovation in each structural
residual, along with two-standard deviations confidence intervals, are presented in Figures 2 and
3 of the appendix. Table 2 reports the time series statistics and values of responses for each
variable.
The results suggest that a depreciating shock to nominal exchange rates have a negative impact
on output growth. The accumulated negative impulse is statistically different than zero over the
first four quarters following the shock. After that, the impact of exchange rate on output
improves but it remains negative and statically not significant. As mentioned before, for various
reasons one must exercise caution in drawing implications from this result, but it suggests that
shocks in the form of nominal exchange rate devaluations have limited ability to spur growth in
the short/medium-run.
Table 2 Time series statistics and responses to one STD innovation
Time Series Statistics
Growth Inflation Interest Exchange
Mean 3,8980 6,6017 5,3378 29,7828
VAR 5,7642 6,1845 7,8172 3,0418
STD 2,4009 2,4869 2,7959 1,7441
Response of Growth to:
Step Growth Inflation Interest Exchange
1 1,7809 -0,4559 -1,1646 -1,0449
2 0,6562 -0,2028 -0,5884 -0,5193
3 0,1865 -0,1123 0,1502 -0,6395
4 0,5372 -0,0915 -0,5231 -0,7666
Response of Inflation to:
Growth Inflation Interest Exchange
1 -0,2470 0,9651 0,6944 0,0132
2 -0,3325 0,2530 0,2665 0,5244
3 -0,4314 0,2251 0,2337 0,9533
4 -0,8508 0,4725 0,7278 0,8822
Response of Interest Rate to:
Growth Inflation Interest Exchange
1 -0,5649 0,6216 0,8639 0,1910
2 -0,6144 0,2669 0,3434 0,7701
3 -0,9707 0,3850 0,5029 1,6004
4 -1,3725 0,9567 1,3561 0,9721
Response of Exchange Rate to:
Growth Inflation Interest Exchange
1 -0,5724 0,0133 0,2156 0,9756
2 -0,9272 0,4497 0,6980 1,0431
3 -0,7365 0,4696 0,4896 0,9260
4 -0,7892 0,5064 0,7417 0,7334
Because of the nature of the exercise, our ability to identify the precise mechanism linking
exchange rate and output fluctuation is limited. So, the focus is on the empirical relationships
and what the data tell us. Nonetheless, the set of estimated impulse responses uncovers empirical
feedback relationships that can be used to shed some light into the policy discussion at hand.
Positive shocks to the nominal exchange rate (depreciating shocks) are found to lead to an
increase in the interest rate. This may reflect monetary policy responding to import inflation
pressures, but that can only be part of the explanation since the interest rate used in the
estimation is not a pure policy variable. More broadly, the evidence suggests that depreciating
shocks to the nominal exchange rate lead to a tighter liquidity stance in the domestic capital
markers, which disfavors output growth. This effect is estimated to be statistically significant
over four quarters, casting some doubt on the feasibility of a crisis strategy based on monetary
loosening combined with non-conventional exchange-rate policy aimed at maintaining the
currency at a weaker level than allowed by the market. Depreciating shocks to the nominal
exchange rate are estimated to lead to inflationary pressures, but statistical significance is not
verified. The results may be interpreted as interest rates responding quickly to nominal exchange
rate fluctuations and cushioning inflationary pressure.
Negative shocks (reduction) to interest rates are found to lead to an increase in output growth.
One could suggest, then, that monetary policy is an effective tool to stimulate the economy. This
result should be interpreted with particular caution. Firstly, the impulse is estimated to be
statistically significant for only one quarter after the shock. Second, it is difficult to rationalize
such an immediate impact of interest rates on output. Normally the transmission mechanism of
monetary policy takes much longer. Because the variable used captures a broader concept of
liquidity stance, it may be possible that it is capturing other short-term factors affecting growth.
In any event, the empirical evidence does not back up monetary policy as a tool with particularly
strong impact on output by itself, which does not mean it cannot play an important role as part of
a coordinated (fiscal and monetary) policy response.
A positive shock to GDP growth also impacts other key macroeconomic variables. Nominal
exchange rate is found to appreciate over four quarters following a growth shock. After that, the
impact is no longer statistically significant. Again, it is difficult to pin down the mechanisms
working behind this result, as our atheoretical structure does not identify the exact nature of the
output shock. However, a possible implication, relevant for the current scenario, would be that a
successfully implemented fiscal stimulus policy could lead to appreciating pressures in the
nominal exchange rate.
4. Conclusion
Coordination between fiscal and monetary policies has been a key measure adopted by the
Government of Mauritius since the world economic crisis started in 2008. A fiscal stimulus
package based predominantly on infrastructure investment coupled with 250 basis point
reduction in the policy rate implemented by the Central Bank will contribute to a soft landing of
the domestic economy. There are limits, however, to what macroeconomic policy can achieve.
This is particularly true in a commodity dependent island economy with small domestic markets
that rely on international trade to achieve its growth potential. The empirical evidence presented
in this paper corroborates the point and, in addition, casts doubt on the extent that a policy of
nominal exchange rate depreciation can sustain short-run growth in Mauritius. Withstanding the
effects of the negative external environment invites a combination of macroeconomic and
microeconomic policy actions. The burden of alleviating heightened social costs associated with
the economic slowdown must be shared among multiple policy instruments. Macro policies will
play a supporting role in aggregate demand management given lack of appetite from the private
sector, but they cannot replace the engines of growth.
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Figure 2 Generalized impulse response functions
Response of growth to growth Response of growth to inf lation Response of growth to interest rate Response of growth to exchange rate
4 4 4 4
3 3 3 3
2 2 2 2
1 1 1 1
0 0 0 0
-1 -1 -1 -1
-2 -2 -2 -2
-3 -3 -3 -3
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Response of inf lation to growth Response of inf lation to inf lation Response of inf lation to interest rate Response of inf lation to exchange rate
Response to Generalized One S.D. Innovations ± 2 S.E.
3 3 3 3
2 2 2 2
1 1 1 1
0 0 0 0
-1 -1 -1 -1
-2 -2 -2 -2
-3 -3 -3 -3
-4 -4 -4 -4
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Response of interest rate to growth Response of growth to inf lation Response of interest rate to interest rate Response of interest rate to exchange rate
6 6 6 6
4 4 4 4
2 2 2 2
0 0 0 0
-2 -2 -2 -2
-4 -4 -4 -4
-6 -6 -6 -6
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Response of exchange rate to growth Response of exchange rate to inf lation Resp. of exchange rate to interest rate Resp. of exchange rate to exchange rate
3 3 3 3
2 2 2 2
1 1 1 1
0 0 0 0
-1 -1 -1 -1
-2 -2 -2 -2
-3 -3 -3 -3
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Figure 3 Accumulated generalized impulse response functions
Accum. Resp. of growth to growth Accum. Resp. of growth to inflation Accum. Resp. of growth to interest rate Accum. Resp. of growth to exchang e rate
6 6 6 6
4 4 4 4
2 2 2 2
0 0 0 0
-2 -2 -2 -2
-4 -4 -4 -4
-6 -6 -6 -6
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accum. Resp. of inflation to growth Accum. Resp. of inflation to inflation Accum. Resp. of inflation to interest rate Accum. Resp. of inflation to exchange rate
15 15 15 15
10 10 10 10
5 5 5 5
0 0 0 0
-5 -5 -5 -5
-10 -10 -10 -10
-15 -15 -15 -15
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Accum. Resp. of interest rate to growth Accum. Resp. of interest rate to inflation Accum. Resp. of interest rate to interest rate Accum. Resp. of interest rate to exchange rate
20 20 20 20
10 10 10 10
0 0 0 0
-10 -10 -10 -10
-20 -20 -20 -20
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Accum. Resp. of exchang e rate to growth Accum. Resp. of exchang e rate to inflation Accum. Resp. of exchang e rate to interest rate Accum. Resp. of exchang e rate to exchang e rate
15 15 15 15
10 10 10 10
5 5 5 5
0 0 0 0
-5 -5 -5 -5
-10 -10 -10 -10
-15 -15 -15 -15
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Figure 4 Ortogonalized impulse response functions
(Cholesky ordering: exchange rate, interest rate, inflation, and growth)
Response of growth to growth Response of growth to inf lation Response of growth to interest rate Response of growth to exchange rate
4 4 4 4
3 3 3 3
2 2 2 2
1 1 1 1
0 0 0 0
-1 -1 -1 -1
-2 -2 -2 -2
-3 -3 -3 -3
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Response of inf lation to growth Response of inf lation to inf lation Response of inf lation to interest rate Response of inf lation to exchange rate
1.5 1.5 1.5 1.5
Response to Cholesky One S.D. Innovations ± 2 S.E.
1.0 1.0 1.0 1.0
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
-0.5 -0.5 -0.5 -0.5
-1.0 -1.0 -1.0 -1.0
-1.5 -1.5 -1.5 -1.5
-2.0 -2.0 -2.0 -2.0
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Response of interest rate to growth Response of interest rate to inf lation Response of interest rate to interest rate Response of interest rate to exchange rate
4 4 4 4
3 3 3 3
2 2 2 2
1 1 1 1
0 0 0 0
-1 -1 -1 -1
-2 -2 -2 -2
-3 -3 -3 -3
-4 -4 -4 -4
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
Response of exchange rate to growth Response of exchange rate to inf lation Response of exchange rate to interest rate Response of exchange rate to exchange rate
2 2 2 2
1 1 1 1
0 0 0 0
-1 -1 -1 -1
-2 -2 -2 -2
-3 -3 -3 -3
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8