EXCHANGE RATE REGIMES AND MACROECONOMIC PERFORMANCEA Summary Of Economic Research project by Zithe Machewere(BSoc.ECO/30/03/10)

OUTLINE OF THE PRESENTATION

Introduction background Problem statement justification Main objectives Specific objectives Hypothesis tested Research questions methodology Econometric results and interpretation Policy recommendations and conclusion

Monday, May 1, 2023 CATHOLIC UNIVERSITY OF MALAWI

INTRODUCTION An exchange-rate regime is the way an authority manages its currency in

relation to other currencies and the foreign exchange market The basic types are a floating exchange rate, a pegged float and a fixed

exchange rate, Macroeconomic performance refers to an assessment of how well a country

is doing in reaching key objectives of government policy The key objectives of the government include; improving living standards of

individuals in an economy through; reduction of unemployment, price stability, interest rates stability and growth in GDP.

Verifiable objective macroeconomic-peformance indicators will include interest rates, inflation, and industrial production indexes a proxy to real GDP

BACKGROUND Historically, Malawi’s exchange rate regimes have predominantly involved a fixed

exchange rate regime. When Malawi gained independence in 1964, the dominant perception was that the flexible exchange rate regime was not desirable for a developing country therefore fixed exchange rate regime was legible. From 1964-1993 there was a fixed exchange rate regime. 1994-2004 there was floating exchange rate regime. 2005-2012 there was a fixed exchange rate regime.

Malawi’s average annual GDP grew by 4.4% from period 1964- 2003, 1994-2003 it grew by 2.3% and from 2004-2009 by 7.1%

Period 1994 to somewhere in 2004 high levels of inflation were recorded as compared to period 2005 to somewhere in 2009 and period 1964 to somewhere in 1994

Source: WDI database (October 2010).

PROBLEM STATEMENT There was an exchange rate regime misalignment between period 1994 to

2004. our background portrays poor macroeconomic performance within that period as compared to period 1964-1993 and period 2005-2012 under a fixed regime.

Poor macroeconomic performance within the later regime was indicated by a lower growth rate of GDP and high inflation as compared to the other regimes.

Misalignment of exchange rate regimes could lead to chaos in an economy as it was with the case of 1997 Asian financial crisis (Philippe. F, 1998).

If the exchange rate regime was well aligned then Malawi’s economy would have had a consistence growth in real GDP accompanied by lower inflation.

Holding other factors constant, the problem was that there was a misalignment of exchange rate regime within the period 1994-2004,.

JUSTIFICATION The empirical study will help policy analysts to provide good

advice when advocating for an exchange rate regime, through

assessing macroeconomic performance underlying each regime.

OBJECTIVES OF THE STUDY The main objective is to empirically identify the exchange rate regime that

has been the most advantageous in terms of improving the macroeconomic performance as with the case of Malawi. The specific objectives of this study are as follows;

o To investigate the impact of fixed exchange rate regime on

macroeconomic performance

o To investigate the impact of flexible exchange rate regime on

macroeconomic performance

HYPOTHESIS TESTS With respect to the objectives above, this study seeks to test the following

null hypothesis;

Fixed Exchange rate regime does not affect macroeconomic performance Flexible Exchange rate regime does not affect macroeconomic performance

Research Questions what are the effects of fixed exchange rate regimes what are the effects of flexible exchange rate regimes

METHODOLOGY Research Strategy adopted is a quantitative data analysis in keeping with the

conventional methods and norms of economic analysis. The data type used for this study is monthly time series(1994-2012), observing structural break, from period 1994-2004(floating regime), and 2005-2012( fixed regime).VAR model will be used to run an OLS regression with e-views 3.1. The generalized equation is given as follows;

Where is a vector of four variables that includes real GDP, inflation, interest rate, and nominal exchange rate. Is the vector of intercepts where as is a white noise error term.

Notably, industrial production indices (IPI) will be used as a proxy to real GDP due to

the unavailability of monthly statistical data of real GDP.

A SUMMARY OF EXPECTED SIGNSExplained variables

Parameters Expected sign(fixed)

Expected sign(flexible)

(IPI)_proxy to real GDP + -Inflation - +Interest rates - +Nominal exchange rates 

+ -

From the economic theory of Optimal Currency Area, we expect interest rates to be negative under a fixed exchange regime and positive under flexible exchange rate regime.( the cost of hedging is Lower under fixed and greater under float).

From the IS-LM Stochastic Model Theory, we expect inflation to be positive under a floating regime and negative under a fixed regime( when exchange rate is fixed the LM shocks have no effect on Inflation and output).

From the Mundell- Fleming Dornbush theory, we expect real GDP to have a negative sign under a floating regime and a positive sign under a fixed regime.( under flexible volatility harmful to trade)

Underlying the assumption of seasonality in monthly time series data, each variable will be seasonally adjusted before conducting a unit root test.

Unit root test will be conducted using ADF test, If, we reject the null hypothesis of non stationarity. If then we do not reject the null hypothesis that the series is non stationary.

A Johansen’s Cointegration test is used in the study to assess whether non-stationary

variables in the model are cointegrated. The null and altanative hypothesis in the test for

Cointegration are; : The series are not cointegrated thus residuals are non-stationary, : The

series are cointegrated thus residuals are stationary.

A granger causality test is conducted to see which variable is able to forecast the other. The granger equation is tested by running a VAR on the system of equations and testing for zero restrictions.

Impulse response functions will be generated through the VAR. The IRF is a process tracing the effects of a shock to each endogenous variable in the system.

Correlogram residual test will be used to test for conditional autocorrelation and heteroscadasticity.

ECONOMETRIC INTERPRETATION AND RESULTSUnderlying the assumption of seasonality each variable is adjusted for seasonal effects and the scale factors are shown in appendix 1.

Unit root tests and Cointegration tests results: Exchange rates, Inflation rates, Interest rates, Industrial Production Variables In levels with

intercept value critical value

1st differences value critical value

Exchange rates 1.741223 (-2.8745)

-4.606545 (-2.8746)**

Inflation rates -2.765797 (-2.8745)

-4.469721 (-2.8746)**

Interest rates -1.759944 (-2.8745)

-5.919881 (-2.8746)**

Industrial production indexes (IPI)

-6.510014 (-2.8745)*

 

Likelihood 5 Percent 1 Percent Hypothesized

Eigenvalue Ratio Critical Value Critical Value No. of CE(s)

0.207243 87.25474 47.21 54.46 None **

0.090209 35.23320 29.68 35.65 At most 1 *

0.044881 14.05623 15.41 20.04 At most 2

0.016690 3.770224 3.76 6.65 At most 3 *

In levels with intercept the unit root tests shows that EXNG, INF & INT are non stationary therefore we accepted the null hypothesis of non stationarity, as for IPI we reject the null. However, in the case of first difference, the null hypothesis of unit root is overwhelmingly rejected for all variables. EXNG, INF & IPI are cointegrated except INT as shown in the Cointegration table results. Appendix 3 shows normalized eq

Granger causality results shows that the exchange rate granger causes interest rate and interest rate does not granger cause exchange rate meaning to say exchange rate is useful in forecasting interest rate. It also shows that inflation granger cause interest rate and interest rate does not granger cause inflation. To see how exchange rate can determine all other three variables that includes; inflation, interest rate and industrial production indexes, impulse response functions analysis is used. The table below shows granger causality results.Pairwise Granger Causality TestsLags: 3 Null Hypothesis: Obs F-Statistic Probability INFSA does not Granger Cause EXNGSA 225 0.15121 0.92883 EXNGSA does not Granger Cause INFSA 1.41829 0.23832 INTSA does not Granger Cause EXNGSA 225 0.28216 0.83825 EXNGSA does not Granger Cause INTSA 8.43788 2.5E-05 IPISA does not Granger Cause EXNGSA 225 0.21124 0.88855 EXNGSA does not Granger Cause IPISA 0.34922 0.78975 INTSA does not Granger Cause INFSA 225 1.21459 0.30529 INFSA does not Granger Cause INTSA 10.0586 3.1E-06 IPISA does not Granger Cause INFSA 225 0.04345 0.98793 INFSA does not Granger Cause IPISA 0.24636 0.86388 IPISA does not Granger Cause INTSA 225 0.07794 0.97188 INTSA does not Granger Cause IPISA 0.76404 0.51533

Traditionally, VAR studies do not report estimated parameters or standard test statistics. Coefficients of estimated VAR

systems are considered of little use in themselves and also the high autoregressive coefficients number of them does not invite

for individual reporting. Instead, the approach of Sims (1980) is often used to summarize the estimated VAR systems by IRFs.

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

EXNGSA INTSA

Response of EXNGSA to One S.D. Innovations

0.0

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5 6 7 8 9 10

EXNGSA INTSA

Response of INTSA to One S.D. Innovations

floating exchange rate regime impulse response 1994-2004

Response of INFSA to One S.D innovation, shows a positive shock of exchange rate from period 1-6 (i.e 1994-2000) has a significant increase in inflation from approximetry 2.5% to about 5.5%, and the effect of the shock from period 7-10 ( 2000-2004) led to a decline from the approximated 5.5% to about 4.3% (by 1.2%). Response of INTSA shows that Interest rate response to shocks in exchange rates from period 1-3 ( 1994-1997) was a sharp decline from about 2.5% to 2% and from period 3-5( 1997-1999), interest rates remained stable ad later declined sharply to about 1.3% from period 5-10 (1999-2004). Response of IPISA shows Industrial production declined sharply in response to changes in exchange rates from period 1-2 and drastically in period 3-5 that is from about 18% to about 0.1%. Industrial production remained stable from period 5-7 and declined to about 0.12% and remained stable from that position to period 10 (2004) in response to a negative shock of exchange rate.

The response of INFSA to one S.D Innovations entails the response of inflation to exchange rate shocks

under a fixed exchange rate regime, from period 1-7(2005-2012), showcased an increase of inflation from

about 0.4% to around 1.3% (by 0.9%). The response of INTSA to one S.D Innovation entails that interest

rates responded by a sharp decline to exchange rate shocks from period 1-7 (2005-2012) by about 1.5% to

0.42%. the response of IPISA entails that Industrial Production dropped sharply within period 1-2 (2005-

2006) by about 9.2% to about –0.9% and started to rise somewhere in between period 2 to period 4 and from

period 4-7 it maintained its stability in production but in a positive response

POLICY RECOMMENDATIONS AND CONCLUSIONThe first suggestion is that under the fixed exchange rate regime, there should be a maintenance

of a tight inflationary measure controls, for instance by the central bank, the control of money

supply. In short term measure should be put in place like devaluing domestic currency i.e.

control devaluation of a currency to keep the domestic currency from gaining value beyond its

required value. In the long run the viable option is insuring export diversification and

improving local and private markets to insure competition both at the local and foreign scene.