Empirical Tests to Discern the Dynamic Causal Chain ... - Science Direct

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Empirical Tests to Discern the Dynamic Causal Chain in Macroeconomic Activity: New Evidence From Thailand and Malaysia Based on a Multivariate Cointegration/Vector Error-Correction Modeling Approach Abul M. M. Masih, University of New South Wales,

Canberra, Australia Rumi Masih, University of Cambridge, United Kingdom The primary aim of this paper is to make an initial attempt to conduct empirical tests in order to discern the dynamic causal c h a i n - i n the Granger (temporal) sense rather than in the structural s e n s e - a m o n g real output, money, interest rate, inflation, and the exchange rate in the context of two small Southeast Asian developing economies, such as Thailand and Malaysia. The methodology employed uses various unit root tests and Johansen's cointegration test followed by vector error-correction modeling, variance decompositions, and impulse response functions in order to capture both the within-sample and out-of-sample Granger-causal chain among macroeconomic activity. Given the relatively stable macroeconomic environment in these two growth-oriented economies, the results, quite in line with our expectations, tend to suggest that in the Granger-causality sense, money supply (particularly MI) appears to have played the leading role of a policy variable being the most exogenous of all, and the other variables including output, rate of interest, exchange rate, and prices appear to have borne most of the brunt of short-run adjustment endogenously in different proportions in order to re-establish the long-run equilibrium. The Granger-causal chain implied by our evidence that money supply (particular M 1) more often predominantly

Address correspondence to Abul M.M. Masih, Department of Economics and Management, University College, The University of New South Wales, ADFA, Northcott Drive, Campbell, A C T 2600, Australia. (e-mail: [email protected]) Authors would like to especially thank: Soren Johansen, Jesus Gonzalo, Katarina Juselius, Deane Terrell, Warwick McKibbin, and Adrian Pagan for their very helpful comments and discussions; and seminar participants at the Malaysian Institute of Economic Research (MIER), University of Malaya (Malaysia), Thammasat University, and United Nations (ESCAP) (Thailand), where earlier versions of this paper were presented. Sam Jegatheswaran provided most able research assistance. The first author would like to acknowledge financial support provided through a University of New South Wales Special Research Grant. The usual disclaimer, of course, applies. Received March 1995; final draft accepted September 1995. Journal of Policy Modeling 18(5):531-560 (1996) © Society for Policy Modeling, 1996

0161-8938/96/$15.00 SSDI 0161-8938(95)00133-6

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leads (rather than lags) output and the other three endogenous variables, is consistent more with the Keynesian (in the case of Thailand) and the Monetarist (in the case of Malaysia) than with the recent Real Business Cycle macroeconomic paradigm. This finding has clear policy implications in the sense that, as long as there is stability and continuation of economic policies (regardless of change of governments) within the framework of a proper macroeconomic discipline (implying thereby an expectation-augmented supply curve being not completely vertical), a monetary expansion in a small developing economy will not necessarily be dissipated merely in terms of higher nominal variables (such as prices, exchange rates, or interest rates) but will contribute positively to assist in achieving an impressive rate of economic growth, as happened in both Thailand and Malaysia for the major part of the period under review. T h e issue is not a choice between rigour a n d intuition, b u t r a t h e r how wellf o u n d e d o n rigour is your intuition.

Jacob Frenkel

1. INTRODUCTION AND THEORETICAL UNDERPINNINGS Valid testing for Granger causality in cointegrated systems is a subject at the very forefront of time-series econometrics (Toda and Philips, 1993. Its importance underlies the critical issue of accounting for the short-term dynamics while preserving any longrun relationships that are bound to manifest among theoretically inferred macro-aggregates over time. Moreover, the recent developments in both the theoretical and applied aspects of modeling for testing causal hypotheses in economics, by offering convenient as well as rigorous tools with an intuitive basis, have stimulated further interest and motivation for econometric modeling, particularly to assist policy formulations and inference. In particular, under the broad area of applied macroeconometrics involving causal inferences, the importance of i'eal versus monetary shocks is currently the most controversial issue in empirical macroeconomics (Caporale, 1994). One of the primary aims of this study is to illustrate in what ways the rapid development of rigorous time-series econometrics may be appropriately exploited to shed light on such a controversial issue and at the same time offer plausible, soundly justified implications based on economic intuition and foresight to assist policy designers. The causal relationship between money and other macroeconomic variables such as output, interest rate, prices, and exchange rate has been in dispute for a long time in mainstream macroeconomics. Different schools of thought have postulated the relationship in different ways, giving rise to different macroeconomic paradigms, such as the Classical, the Keynesian, the Monetarist, the

DYNAMIC CAUSAL CHAIN IN MACROECONOMIC ACTIVITY

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New Classical, the New Keynesian, and finally, the recent Real Business Cycles. Up until the recent Real Business Cycle theory, the dominant common theme running across these doctrines (with the exception of the Classical, who believed that an increase in money supply would in the long run result only in a proportionate increase in the price level without any increase in economic activity, P~-M) was that an aggregate demand shock such as monetary shocks would have a positive effect on real economic activity. In other words, money would lead (rather than lag) economic activity. The issues among the Keynesian, the Monetarist, the New Classical, and the New Keynesian were not whether or not monetary shocks had a positive effect on output but the nature and the transmission channels of these positive shocks. The Keynesian believed that a positive monetary shock would increase both economic activity and price level through the interest rate and investment variables (Y~P~I~R~M). Led by Friedman, the Monetarist integrated the Keynesian short-run theory with the Classical long-run theory. In the short run, they agreed with the Keynesian transmission channel (Y~-M), but if the monetary expansion is sustained in the long run, they agreed with the Classical as to the long-run neutrality of money (P"--R~ Y*-M), because monetary expansion would then be dissipated in terms of higher interest rates and prices rather than output, which would return to "natural level" as soon as the inflationary expectations have been fully adapted. The expectation-augmented long-run supply curve, according to them, will be fully vertical, although in the short run it could be upward-sloping, as is postulated by the Keynesian. The New Classical, led by Barro, Lucas, Sargent, and Wallace, decomposed monetary effect into output and price effect, not on the basis of short and long run but on whether the monetary expansion is "anticipated" or "unanticipated." Based on the concept of "rational expectations" and equilibrium "efficient market" hypothesis, they postulated that only the unanticipated monetary expansion would result in an increase in output, but the anticipated increase in money would be dissipated in inflation; that is, according to them, the expectation-augmented supply curve is vertical both in the short as well as in the long term. The New Keynesian, however, based on the hypotheses of rational expectations but disequilibrium inefficient market, postulated non-neutrality of money at least in the short run because of rigidities in prices and wages, and market failures and imperfections. In sharp contrast to these competing paradigms, the recent Real Business

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Cycle (RBC) theory is the latest incarnation of the "classical dichotomy" in that monetary expansion cannot increase real output. The RBC economists view the historical association between money and output as the case of money supply endogenously responding (rather than leading) to an increase in output. To the RBC school, money-output correlations observed in the data should be attributed to "reverse causation." That is, the banking sector responds to increased demand for transactions by creating more inside money. To them, monetary expansion, whether short or long run (as focused by the Monetarist) and anticipated or unanticipated (as focused by the early New Classical school), will have no positive effect on output; it will only raise interest rates and the price level. The RBC school, therefore, views money supply as endogenous and a function of output that is determined exogenously by factors such as technology or other real "stochastic" shocks (P"--R ~ M,--IO.1 Hence, the causal chain (among money and other macroeconomic activity such as output, interest rates, and price level) implied by the existing macroeconomic paradigms still remains ambiguous. The issue, therefore, as to the dynamic causal relationships (even in the Granger temporal sense rather than in the structural sense) remains unresolved and is an empirical one. 2 In order to empirically resolve the issue of the direction of causation in a bivariate context, a lot of causality tests have been applied based mainly on the standard Granger (1969), Sims (1972), and the modified Sims suggested by Geweke, Meese, and Dent (1983). But the studies applying these tests suffered from the following methodological deficiencies:

For a good discussion on the strengths and limitations of different schools of thought in macroeconomics, see the papers of symposiums on "Keynesian Economics Today" and on "Real Business Cycle"in the Journal of Eeonomic Perspectives, Winter 1993 and Summer 1989, respectively. Also the theoretical and empirical works both for and against the monetary neutrality proposition are available in a number of good survey articles, such as Oxley and McAleer (1993), Mullineux and Dickenson (1992), Mankiw (1990), Gordon (1990), Honkapohja (1990), and Greenaway (1989). 2Causality is a subject of great controversy among economists. See, for example, Zellner (1988). Interested readers could refer to a supplementary issue of the Journal of Econometrics, September-October 1988, that includes studies discussing this issue. Without going into the debate, we would like to state that the concept used here is in the stochastic or "probabilistic" sense rather than in the philosophical or "deterministic" sense. Also the concept used here is in the Granger "temporal" sense rather than in the "structural" sense.

DYNAMIC CAUSAL CHAIN IN MACROECONOMIC ACTIVITY

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1. These standard tests did not examine the basic time-series properties of the variables. If the variables are cointegrated, then these tests incorporating differenced variables will be misspecified unless the lagged error-correction term is included (Granger, 1988). 2. These tests turn the series stationary mechanically by differencing the variables and consequently eliminated the long-run information embodied in the original level form o f the variables. The error-correction model (ECM) derived from the cointegrating equations, by including the lagged errorcorrection term reintroduces, in a statistically acceptable way, the long-run information lost through differencing. The errorcorrection term stands for the short-run adjustment to longterm equilibrium trends. This term also opens up an additional channel of Granger causality so far ignored by the standard causality tests. 3. Moreover, although recently there has been a beginning of the application of ECM in causality testing in the bivariate context, such as Masih and Masih (1994b, 1995d), BahmaniOskooee and Alse (1993), and Miller and Russek (1990), there has been very little attempt at testing the Granger-causality channel in a dynamic multivariate context through vector error-correction modeling (VECM), variance decompositions (VDCs), and impulse response functions (IRFs). The primary purpose of this paper is to conduct empirical tests to discern the dynamic causal r e l a t i o n s h i p s - i n the Granger (temporal) sense rather than in the structural s e n s e - among money and other macroeconomic variables such as output, interest rate, and prices in the context of two small Southeast Asian developing economies, such as Thailand and Malaysia. In order to examine the dynamic interactions of these variables with the foreign trade sector, we want to incorporate the foreign exchange rate variable as well. As mentioned before, at the m o m e n t very few works exist on the application of ECM in testing Granger causality. But even these few works are set in a bivariate context, and although they may serve as useful pre-test statistics prior to model construction, it is difficult to obtain anything very meaningful to reach policy conclusion on the basis of such tests. Moreover these studies do not attempt to quantify results of Granger causality by unearthing deeper insights through the application of variance decompositions and impulse response functions. This study will make an attempt to

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improve and extend the existing few ECM-based works on Granger causality in the following ways: 1. It will try to discern Granger causality in small developing economies in a multivariate framework and within the environment of vector error-correction modeling. This analysis will also make use of the techniques-variance decompositions, and impulse response f u n c t i o n s - t o unveil Granger causality in macroeconomic activity in a dynamic context) 2. The error-correction terms derived from the cointegrating vectors are arrived through Johansen's multivariate cointegrating testing procedure (in contrast to much of the preexisting literature), which are then used as additional channels in order to identify Granger causation. Because this procedure, unlike the Engle-Granger approach, identifies multiple cointegrating relationships and hence error-correction terms, this is an issue of crucial importance in Granger-causality testing in a dynamic multivariate context? 2. ECONOMETRIC METHODOLOGY The following sequential procedures will be adopted:

Step 1: Cointegration and Granger (Temporal) Causality The cointegration technique pioneered by Engle and Granger (1987), Hendry (1986), and Granger (1986) made a significant contribution towards testing Granger causality. Two or more variables are said to be cointegrated, that is, they exhibit long-run equilibrium relationship(s), if they share common trend(s). (For an an application of this technique in related disciplines, see Masih and Masih, 1995b, 1995c). According to this technique, if two variables are cointegrated, the finding of no-causality in either d i r e c t i o n - o n e 3In an effort to examine the relationship between money and income, or money and interest rate, some recent works such as Kamas and Joyce (1993), Krol and Ohanian (1990), Stock and Watson (1989), Gan (1988), and Kang (1987) have used multivariate causality tests and VARs including VDCs and IRFs. However, unlike ours, these causality tests were not conducted within the framework of Johansen's cointegrating tests and VECM. Miller (1991) and Ambler (1989) applied VECM in multivariate causality tests but did not apply Johansen's tests, VDCs and IRFs. 4Although this is more apparent for multivariate systems or relationships, the Johansen procedure has been used extensively in various bivariate studies (for example, see Masih and Masih, 1994a, 1995b, indicating more robust findings in contrast to the residual-based single equation Engle-Granger OLS approach.

D Y N A M I C C A U S A L C H A I N IN M A C R O E C O N O M I C

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of the possibilities with the standard Granger (1969) and Sims (1972) t e s t s - i s ruled out. As long as the two variables have a common trend, causality (in the Granger sense, not in the structural sense), must exist in at least one direction, either unidirectional or bidirectional (Granger, 1986, 1988). Evidence of cointegration among variables also rules out the possiblity of the estimated relationship being "spurious." However, although cointegration indicates the presence or absence of Granger causality, it does not indicate the direction of causality between variables. This direction of the Granger (or temporal) causality can be detected through the vector errorcorrection model derived from the long-run cointegrating vectors.

Step 2: Vector Error-Correction Modeling (VECM) and Exogeneity Engle and Granger (1987) demonstrated that once a number of variables (say, xt and yt) are found to be cointegrated, there always exists a corresponding error-correction representation, which implies that changes in the dependent variable are a function of the level of disequilibrium in the cointegrating relationship (captured by the error-correction term) as well as changes in other explanatory variables(s). A consequence of ECM is that either Art or Ayt or both must be caused by et-1 (the equilibrium error) which is itself a function of xt-~, y , - l . Intuitively, if yt and xt have a common trend, then the current change in xt (say, the dependent variable) is partly the result of xt moving into alignment with the trend value of y, (say, the independent variable). Through the error-correction term, the ECM opens up an additional channel for Granger causali t y - ignored by the standard Granger (1969) and Sims (1972) tests to emerge. The Granger causality (or endogeneity of the dependent variable) can be evidenced either through the statistical significance of the t-test of the lagged error-correction term(s) and/or the F-test applied to the joint significance of the sum of the lags of each explanatory variable. The non-significance of both the t-tests(s) as well as the F-tests in the VECM indicates econometric exogeneity of the dependent variableJ 5The VAR, being a system of unrestricted reduced-from equations, has been criticized by Cooley and Leroy 0985). Runkle (1987) is a good example of the controversy surrounding this methodology. Backus (1986) and Ambler 0989) are examples defending the use of VAR. It is debatable whether the method of identification employed by the simultaneous equation structural model, which often relies on many simplifying assumptions and arbitrary exclusion restrictions together with the related exogenous-endogenous variables classification (which are often untested) is superior to the identification procedure used in the VAR model.

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In addition to indicating the direction of causality among variables, the VECM approach allows us to distinguish between "shortterm" and "long-term" Granger causality. When the variables are cointegrated, then in the short term, deviations from this long-term equilibrium will feed back on the changes in the dependent variable in order to force the movement towards the long-term equilibrium. If the dependent variable (say, the change in the money supply) is driven directly by this long-term equilibrium error, then it is responding to this feedback. If not, it is responding only to shortterm shocks to the stochastic environment. The F-tests of the "differenced" explanatory variables give us an indication of the "short-term" causal effects, whereas the "long-term" causal relationship is implied through the significance or otherwise of the t-tests(s) of the lagged error-correction term(s), which contains the long-term information because it is derived from the long-term cointegrating relationship(s). The coefficient of the lagged errorcorrection term, however, is a short-term adjustment coefficient and represents the proportion by which the long-term disequilibrium (or imbalance) in the dependent variable is being corrected in each short period. Nonsignificance or elimination of any of the "lagged error-correction terms" affects the implied long-term relationship and may be a violation of theory. The nonsignificanceof any of the "differenced"variables, which reflects only short-term relationship, however, does not involve such violations because theory typically has nothing to say about short-term relationships (Thomas, 1993). By example, applied work employing this formulation has been used to test for the causal chains implied by the major paradigms in macroeconomictheory in the case of NICs (see Masih and Masih, 1995a).

Step 3: Variance Decompositions (VDCs) and Relative Exogeneity The VECM F- and t-tests may be interpreted as within-sample causality tests. They can indicate only the Granger exogeneity or endogeneity of the dependent variable within the sample period. They do not provide an indicator of the dynamic properties of the The critics of VAR, however, all agree that there are important uses of the VAR models. For example, McMillin (1988) points out that VAR models are particularly useful in the case of "forecasting, analyzing the cyclical behaviour of the economy, the generation of stylized facts about the behaviour of the elements of the system which can be compared with existing theories or can be used in formulating new theories, and testing of theories that generate Granger-causality implications."

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system, nor do they allow us to gauge the relative strength of the Granger-causal chain or degree of exogeneity among the variables beyond the sample period. VDCs that may be termed as out-ofsample causality tests, by partitioning the variance of the forecast error of a certain variable (say, money supply) into proportions attributable to innovations (or shocks) in each variable in the system, including its own, can provide an indication of these relativities. A variable that is optimally forecast from its own lagged values will have all its forecast error variance accounted for by its own disturbances (Sims, 1982). 6 Step 4: Impulse Response Functions (IRFs) The information contained in the VDCs can be equivalently represented by IRFs. Both are obtained from the MA representation of the original VAR model. IRFs essentially map out the dynamic response path of a variable due to a one-period standard deviation shock to another variable. The IRFs are normalized such that zero represents the steady-state value o f the response variable. 7 *The results based on VARs, and VDCs are generally found to be sensitive to the lag length used and the ordering of the variables. A considerable time has been spent in selecting the lag structure through FPE criterion. FPE method is based on an explicit optimality criterion of minimizing the mean squared prediction error. The criterion tries to balance the risk due to bias when a low order is selected, and the risk due to increase in the variance when a higher order is selected. By construction, the errors in any equation in a VAR are usually serially uncorrelated. However, there could be contemporaneous correlations across errors of different equations. These errors were orthogonalized through Choleski decomposition. In order to orthogonalize the innovations, a predetermined triangular ordering of the five variables had to be made. In a small developing economy like Thailand and Malaysia, which are affected heavily by the vagaries of exports, one would expect the financial variables to respond quicker than the real variables. Moreover, in line with the suggestion made by Gordon and King (1982) that variables that respond most to current events, such as changes in the nominal exchange rates and interest rates, should he placed last in the order so that their values reflect contemporaneous realization of variables of a higher order, the innovations were orthogonalized in the following order: Y, MI(2), R, P, E, where E represents the exchange rate. With the residual variance-covariance matrix being near diagonal, the results were not sensitive to alternative ordering of the variables based on alternative macroeconomic paradigms. 7The Impulse Response Functions, like the variance decompositions, were also obtained from the unrestricted VAR form of the model, although they could be computed via a dynamic multiplier analysis of VAR systems with cointegration constraints (see Lutkephol, 1991, and Lutkephol and Reimers, 1992. To trace the dynamic effects of various shocks, the estimated VECM is reparameterized to its equivalent formulation in levels. With this reparameterization, the error-correction terms are incorporated into the first-period lagged terms of the autoregression. The model is then inverted to obtain the impulse response functions that capture the effects of deviations from long-run equilibrium on the dynamic

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3. ESTIMATION RESULTS The model consists of five variables; real output, money (M1 and M2), interest rate, prices, and exchange rate. The primary source of all these data is the International Financial Statistics published by the IMF. The data are annual covering the period 195591 for Thailand and Malaysia. g A necessary but not sufficient condition for cointegration is that each of the variables should be integrated of the same order (more than zero) or that all series should contain a deterministic trend (Granger, 1986). A wide range of unit root tests (Table 1) wereapplied (some of which are not presented here due to space) to test the order of integration of the variables. Results in Table 1 tend to indicate that for all variables concerned, we cannot reject the presence of a unit root for any of the variables. Therefore, overall, we could not find evidence that the variables are not I(1), that is, variables were found nonstationary at the "level" form but stationary after "first-differencing." The results based on Johansen's (Johansen, 1988; Johansen and Juselius, 1990) multivariate cointegration tests (Table 2) tend to suggest that these five variables are bound together by long-run equilibrium relationship(s). In the case of Thailand, the number is one for the M 1 model, and two in the M2 model as indicated by the tests of the null hypothesis through the maximum eigenvalue or trace tests. However, in the case of

paths followed by a variable in response to initial shocks. Intuitively, IRFs trace the response over time of a variable, say x, due to a unit shoek given to another variable, say y. A similar procedure is adopted, among others, by Robertson and Orden(1990). 8We employ annual data (rather than quarterly or monthly) primarily for the following reasons: 1. While annual observations yield smaller degrees of freedom, monthly or even quarterly time intervals could be too short to reflect the natural interval between money and output. Because noisy effects tend to average out with annual data, this facilitates better examination of the money-output linkage. See also Granger (1977) for similar justifications in support of using low-frequency data. 2. Shiller and Perron (1985) argue strongly that, particularly when analyzing the long-run characteristics of economic time series, the length of the time series is far more important than the frequency of observations. See also Hakkio and Rush (1991) for similar observations. 3. Finally, real GDP data for most of the Asian developing economies including Thailand and Malaysia (since the mid-1950s or early 1960s) are available only annually. For further justification of applying time-series techniques to more temporally aggregated data, specifically in our context, see Spencer (1989).

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Malaysia, the number is two in the M1 model and one in the M2 model. 9 Evidence of cointegration among these five macro-aggregates has several implications. First, its rules out "spurious" correlations and also the possibility of Granger noncausality, which in turn implies at least a unique channel for Granger causality to emerge (either unidirectional or bidirectional). Secondly, the actual number of cointegrating (or equilibrium) relationship(s) found in Table 2 will result in a corresponding number of residual series and hence errorcorrection terms (ECTs), which we may embed as exogenous variables appearing in their lagged levels as part of the vector errorcorrection model (VECM) in Table 3. Thirdly, cointegration also rules out the use of modeling any dynamic relationships through ordinary first-differenced VARs as these will be misspecified, and also structural VARs (see Rogers and Wang; 1993, for applications), as these models do not impose cointegration constraints. '° These and many other related issues are discussed by King et al. ( 1991 ) and To da and Phillips (1993), who support the use o f VECMs in empirical tests of Granger causality. As stated earlier, cointegration among variables cannot indicate the direction of Granger causality inherent between them. For purposes of causality tests, we turn to results provided by the withinsample VECM (Table 3). For Thailand, the VECM including M 1 tends to indicate that of all the variables M 1 and interest rate stand out econometrically exogenous as illustrated through the statistical significance or otherwise of both the t-tests of the error-correction term and the F-tests of the independent variables. Given the use of M1 as the policy variable and the control on interest rates by 9Coefficients of cointegrating vectors are often difficult to interpret, especially in the context of economic theoretical inference. Our purpose in this paper is to test certain causal hypothesis using the long-run cointegrating coefficients to construct a residual term that is embedded as part of the VAR. With this in mind, these vectors are not presented, though available upon request. ~0Wang, Yip, and Scotese (1994) is another example of an application of a structural VAR model with imposed long-run restrictions based on theoretical predictions of endogenous consumption, labor-leisure, and fertility. However, in order to arrive at the structural VAR, various univariate and multivariate stationarity tests were performed on the data because "In addition to stationarity, the structural VAR requires that there exists no cointegrating relationships between the endogenous variables." (Wang et al., 1994, p. 261) (italics added). However, as noted by Karras (1994, p. 1768), "If cointegration relationships are found to exist, the model must be estimated by the Vector Error Corrections Model (VECM) examined by Engle and Granger (1987) and more recently used by King et al. (1991)."

Root

- 1.794

-2.520

- 1.813

-0.403

- 0.594

-0.732

- 1.103

-0.879

-0.524

-0.108

- 1.258

-2.341

- 1.811

-2.127

- 1.737

-3.029

-2.781

-2.490

-0.653

-2.165

- 1.053

Y

MI M2 IR CP ER

- 1.003

-2.437

Z(a)

-2.751

-0.580

-3.278

-0.546

-0.983

-0.278

- 1.398

-0.092

-6.033

-0.725

-0.704

-0.232

Hypothesis

Aug Dickey-Fuller ~, %

of the Unit

- 1.725

Tests

Y

l:

M1 M2 IR CP ER

Table

- 1.280

-0.816

- 1.542

- 1.095

- 1.803

-0.688

Annual

- 0.573

-0.125

- 1.806

-3.063

- 1.576

-0.881

Annual

Z(ta)

0.938

7.945***

1.331

17.472"**

16.282"**

31.136"**

Data (Malaysia)

0.514

7.796**

1.637

28.161"**

30.374***

84.232***

Data (Thailand)

Z(~l)

-2.116

-3.885

- 1.939

-6.009

-5.006

- 1.288

- 5.476

-3.963

- 8.124

-2.996

- 5.883

- 11.167"

Phillil~s-Perron Z(a*)

- 1.132

-1.876

-0.665

-2.939

-2.128

-2.052

- 1.733

- 1.560

- 1.842

-2.061

- 1.848

-2.606

Z(t..)

0.674

7.964***

0.966

23.221"**

20.089***

28.685***

1.601

6.408**

1.263

66.720***

23.175"**

4.790

Z(02)

0.900

3.026

1.306

3.513

8.232

5.250

2.029

1.409

1.888

4.199

3.534

4.024

Z(03)

~r

5

5~r.

;>

Sig Level 107o 5070 10070

-4.38 -3.60 -3.24

•,

-3.75 -3.00 -2.63

~.

-22.50 -17.90 -15.60

Z(a)

-4.38 -3.60 -3.24

Z(t~)

7.88 5.18 4.12

(n = 25)

Critical Values Z(O~)

-17.20 -12.50 -10.20

Z(a*)

-3.75 -3.00 -2.63

Z(to.)

8.21 5.68 4.67

Z(09

10.61 7.24 5.91

Z(03)

Notes: Definitions: gross domestic product (Y); money supply defined as money in circulation and cash deposits held in banks (MI); Ml plus time, savings and foreign currency deposits of residents (M2); discount interest rate (IR); consumer price index (CP); spot exchange rate ( E R ) . The data set consisted of 36 observations from 1955-1991 inclusive, sourced from various issues of I n t e r n a t i o n a l F i n a n c i a l S t a t i s t i c s . The optimal lag used for conducting the Augmented Dickey-Fuller test statistic was selected based on minimizing the Akaike's FPE using a window choice of w(s, /) = 1 - [ s / ( l + 1)] where the order is the highest significant lag from either the autocorrelation or partial autocorrelation function of the first differenced series. Tests were repeated on first-differences of each variable in order to confirm that all were I(1). Results of these and other tests are available from authors upon request. Relevant test questions and related technical descriptions for all unit root testing procedures appear in Appendix: A1. ***,** and * indicate significance at the 1%, 5070 and 10% levels.

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56.307** 28.983** 16.463 6.701 3.157

41.441"* 26.794 11.542 5.372 0.585

A

111.612"* 55.301"* 26.321 9.858 3.157

86.734** 45.293 17.498 5.957 0.585

Trace

[II, MI, IR, CP, ERI

58.450** 23.112 13.633 7.876 1.783

40.728** 34.574** 15.773 7.263 0.882

A

104.903"* 46.404 23.292 9.659 1.783

99.219"* 58.491"* 23.918 8.145 0.883

Trace

[Y, M2, 1R, CP, ERI

33.461 27.067 20.967 14.069 3.762

33.461 27.067 20.967 14.069 3.762

A

68.524 47.210 29.680 15.410 3.762

68.524 47.210 29.680 15.410 3.762

Trace

Critical Values (95070)

^ refers to the max imum eigenvalue test statistic; r indicates the n u m b e r of cointegrating relationships. The coefficients of the cointegrating vectors, though not reported, are available upon request. Critical values are taken from Johansen and Juselius (1990). ** indicates rejection at the 95°70 critical values.

Thailand r = 0 r -< 1 r _< 2 r _< 3 r _< 4 Malaysia r = 0 r _< 1 r _ 2 r _< 3 r _< 4

Ho:

Vector:

T a b l e 2: J o h a n s e n ' s T e s t f o r M u l t i p l e C o i n t e g r a t i n g V e c t o r s

.>

t~

DYNAMIC

CAUSAL CHAIN

IN MACROECONOMIC

ACTIVITY

545

3A: Temporal Causality Results Based on Vector Error-Correction Model (VECM) (Thailand) Table

MI Model

AY

Dep Variable AY AM1 AIR ACP AER

0.715 0.678 0.661 0.177

M2 M o d e l

AY

Dep Variable AY AM2 AIR ACP AER

0.949 0.379 0.736 0.584

AM1

ACP

AER

~l(Vt- ~)

F-statistics (Sig Levels) 0.033** 0.575 0.217 0.996 0.802 0.462 0.721 0.164 0.666 0.065* 0.026** 0.497

0.478 0.659 0.813 0.519 -

t-statistics -2.145"* 0.639 1.418 - 1.746" -2.931"**

A CP

AER

~1(¥t- i)

F-statistics (Sig Levels) 0.577 0.739 0.591 0.455 0.950 0.623 0.467 0.778 0.471 0.591 0.621 0.430

0.192 0.886 0.892 0.875 -

t-statistics 1.393 1.010 0.915 - 1.855* 1.897 0.379 -0.536 -2.617"* - 1.264 -0.711

AM2

AIR

AIR

*z(~t- t)

All variables are in first differences (denoted by A) with the exception of the lagged error-correction terms [e(q/t- 1)] generated from Johansen order of cointegration tests conducted in Table 2. The ECTs were derived by normalizing the two cointegrating vectors on Y, thereby resulting in one(two) set(s) o f residuals for the MI(M2) model. Prior to normalizing we tested restrictions upon each o f the five variables to check that they enter the cointegrating relationship significantly. The residuals were also checked for stationarity by way of unit-root testing procedures applied earlier and inspection of their autocorrelation functions respectively, the V E C M s were based on (FPE criteria optimally determined) 2-year lag structures a n d a constant for both the M 1 a n d M2 models. Diagnostic tests (not reported) conducted for various orders of serial correlation, heteroskedasticity, functional form, and normality were overall found to be satisfactory. ***, ** a n d * indicate significance at the 1°7o, 5o70 and 10o70 levels.

the Bank of Thailand over a large part of the period under consideration, it is unsurprising that M1 and interest rate variables were relatively the leading variables. Intuitively, the mechanics behind the VECM results imply that both these variables were the initial receptors of exogenous shocks to the long-term equilibrium relationship, and all the remaining variables including output, exchange rate, and prices had to bear the burden of short-run adjustment (to long-term trend) endogenously in different proportions in order to re-establish the long-term equilibrium. As to the VECM (M2 model), one feature that is strikingly common is the exogeneity of the interest rates. As stated earlier, this might be due to the institutional regulatory reasons. For Malaysia, however, the VECM exercise

546

A.M.M.

M a s i h a n d R. M a s i h

T a b l e 3B: T e m p o r a l C a u s a l i t y R e s u l t s B a s e d o n V e c t o r E r r o r - C o r r e c t i o n Model (VECM) (Malaysia)

M1 Model

AY

AMI

-0.105" 0.144 0.902 0.625

0.160 0.062* 0.357 0.602

0.515 0.551 0.144 0.414

AIR

Dep Variable AY AMI

AIR ACP

AER

AIR

ACP

AER

F-statistics (Sig Levels)

M2 Model

AY

AM2

Dep Variable AY AM2 AIR ACP AER

0.148 0.352 0.366 0.691

0.527 0.103" 0.051"* 0.032**

0.014"* 0.500 0.336 0.728

ACP

0.015"* 0.436 0.617 0.062*

~2(~t- t)

t-statistics 0.330 0.919 0.375 0.682 -

AER

F-statistics (Sig Levels) 0.054** 0.879 0.698 0.783

~t(~t- 1)

- 1.073 - 1.983" 1.948" -3.283"** - 1.091

-2.407** -0.783 0.779 -2.606"* 1.494

~l(~t-l) t-statistics

0.962 0.035** 0.306 0.288 -

- 1.307 4.267*** - 1.713" 1.357 0.365

All variables are in first db~ferences (denoted by A) with the exception of the lagged error-correction terms [~Wt- 1)] generated f r o m Johansen order of cointegration tests conducted in Table 2. The ECTs were derived by normalising the two cointegrating vectors on Y, thereby resulting in two(one) and one set(s) of residuals for the MI(M2) model. Prior to normalizing we tested restrictions upon each of the five variables to check that they enter the cointegrating relationship significantly. The residuals were also checked for stationarity by way of unit-root testing procedures applied earlier and inspection of their autocorrelation functions respectively, the V E C M s were based on (FPE criteria optimally determined) 4 and l-year lag structures and a constant, for the MI and M2 models respectively. Diagnostic tests (not reported) conducted for various orders of serial correlation, heteroskedasticity, functional form and normality were overall found to be satisfactory. ***, ** and * indicate significance at the 1o70, 5°70 and 10% levels.

for both M 1 and M2 definitions o f money is indicative o f the econometric endogeneity o f all variables (with the exception of the exchange rate in the M 1 model). 1~ However, as stated earlier, although the VECM can help us discern the exogeneity or endogeneity o f a variable and also can give us an understanding o f the direction o f Granger causality within the sample period, it does not provide us with an indication o f the ~ A VAR, being a closed system, may be misspecified in some of the equations. For example, the exchange rate cannot be explained by only domestic variables on the one hand, because import prices may on the other hand heavily influence a small developing economy. In this case, output probably picks up most o f the exogenous import price shocks and m a y therefore show itself up as weakly exogenous.

DYNAMIC CAUSAL CHAIN IN MACROECONOMIC ACTIVITY

547

dynamic properties of the system, nor does it allow us to gauge the relative strength of the variables beyond the sample period. While the earlier VECM analysis could be thought of as a withinsample causality test, the VDCs (Tables 4 and 5) could be deemed to be an exercise of an out-of-sample causality test. An analysis of the dynamic interactions of various shocks in the post-sample period is brought to light through the VDCs and IRFs (Tables 4 and 5 and Figures 1 and 2). The Granger-causal chain implied by the analysis of VDCs tends to suggest that in both these countries, money supply (particularly M1) is relatively the leading variable, being the most exogenous of all. For example, in the case of Thailand, in the M1 model even after a 5- or 10-year horizon, about 83 percent of the forecast error variance of M1 is explained by its own shocks (compared to 59 percent in the case of output). Similarly, in the case of Malaysia, about 60 percent of the forecast error variance of M1 is explained by its own shocks (compared to 36 percent in the case of output). Furthermore, a cross-check of variance decompositions (Table 4) in Thailand indicates that while output explains only about 4 percent of the variance of M1 at the 5- or 10-year horizon, M 1 explains about 17 percent of the variance of output. In the case of Malaysia (Table 4), while output explains 23 percent of the variance of M1 and the 5- or 10-year horizon, M 1 explains 39 percent of the variance of output. In decompositions of the model including M2, although the variances accounted for by one's own shocks in the case of M2 and output, respectively, are not markedly different, M2 still maintains its lead over output (in the case of Thailand), and they are equally strong (in the case of Malaysia). An analysis of the impulse response functions (IRFs) are presented in Figures 1 and 2 and tend to suggest that in both these countries, a shock to either M1 or M2 results in some positive response in output (at least in the short run), although accompanied by sensitivities in some nominal variables as well, such as exchange rates, prices, and interest rates. Therefore, the IRFs appear to be broadly consistent with the earlier VDC results that money supply (especially M1) more often leads (rather than lags) real output in both of these countries. During a major part of the period under consideration, both Thailand and Malaysia had maintained a relative macroeconomic stability by Southeast Asian standards. For example, in the case of Thailand, during the 1970s and 1980s, her average inflation rate was 7.0 percent (Southeast Asian average being 11 °70), budget deficit

548

A.M.M.

M a s i h a n d R. M a s i h

Table 4A: D e c o m p o s i t i o n o f V a r i a n c e for M I M o d e l (Thailand)

Years

Percentage of Forecast Variance Explained by Innovations in: AY AMI AIR A CP A ER

Relative Variance in: AY

1

100.00 86.34 60.09 59.46 59.81 59.00

0.00 1.13 16.42 16.80 16.65 16.75

0.00 1.45 12.56 12.87 12.72 13.40

0.00 11.08 10.76 10.56 10.44 10.38

0.00 0.00 0.16 0.31 0.37 0.46

Relative variance in: AM1 1 2 3 4 5 10

0.15 3.64 3.78 3.70 4.06 4.21

99.85 93.04 88.47 88.27 83.45 80.15

0.00 1.58 4.09 4.21 7.05 8.41

0.00 0.02 0.20 0.38 0.95 2.33

0.00 1.54 3.45 3.42 4.48 4.90

Relative variance in: A I R 1 2 3 4 5 10

0.02 1.51 1.53 1.65 1.71 1.75

15.83 17.34 19.60 19.57 19.48 19.24

84.15 79.39 75.07 74.04 72.45 72.09

0.00 0.42 0.73 1.48 2.37 2.66

0.00 1.34 3.06 3.34 3.75 4.25

Relative variance in: A C P 1 2 3 4 5 10

0.07 0.65 0.82 1.12 1.10 1.46

5.90 33.40 42.26 39.31 38.68 36.64

33.84 22.35 19.24 20.54 20.19 21.87

60.18 39.41 34.03 33.36 34.41 33.70

0.00 4.19 3.65 5.66 5.61 6.33

Relative variance in: A E R 1 2 3 4 5 10

6.01 2.69 3.67 4.60 4.49 4.78

1.90 1.54 2.79 3.09 3.20 3.24

3.58 39.68 42.37 42.84 41.29 41.95

14.86 12.46 11.16 12.89 15.59 16.29

73.64 43~.64 40.01 36.57 35.42 33.74

2 3 4 5 10

Notes: Figures in the first column refer to horizons (i.e., number of years). All other figures are estimates rounded to two decimal places; rounding errors may prevent a perfect percentage decomposition in some cases. Several alternative orderings of these variables were also tried with monetary variables appearing prior to the output variable. Such alterations, however, did not alter the results to any substantial degree. This is possibly due to the variance-covariance matrix of residuals being near diagonal, arrived at through Choleski decomposition in order to orthogonalize the innovations across equations.

D Y N A M I C C A U S A L C H A I N IN M A C R O E C O N O M I C

ACTIVITY

549

T a b l e 4B: D e c o m p o s i t i o n o f V a r i a n c e for M1 M o d e l (Malaysia)

Percentage of Forecast Variance Explained by Innovations in: Years

AY

A/Ill

AIR

ACP

AER

Relative Variance in: AY

1

100.00

0.00

2 3 4 5 10

58.80 53.34 49.18 36.49 36.66

24.32 25.66 25.08 39.00 38.65

0.00 9.61 11.89 12.00 15.01 14.20

0.00 2.77 4.18 8.50 5.99 6.45

4.50 4.93 5.24 3.52 4.03

Relative Variance in: AM1 1 2 3 4 5 10

27.38 16.99 25.56 24.77 23.41 23.98

72.62 75.74 60.09 58.25 60.10 59.18

0.00 6.44 11.07 11.26 10.91 10.86

0.00 0.81 1.36 2.98 2.81 2.77

0.00 0.02 1.93 2.73 2.76 3.20

Relative Variance in: A I R 1 2 3 4 5 10

0.66 9.20 12.82 22.46 20.53 20.60

2.90 45.00 43.04 36.76 40.77 42.62

96.44 42.79 41.29 37.97 35.70 32.18

0.00 0.00 0.14 0.11 0.68 0.87

0.00 3.01 2.70 2.70 2.34 3.72

Relative Variance in: A C P 1 2 3 4 5 10

0.47 3.82 10.55 13.12 12.96 13.07

8.87 30.52 45.86 46.11 45.87 46.45

14.69 31.89 23.70 21.74 21.51 21.21

75.95 33.75 19.83 18.99 19.23 18.73

0.00 0.02 0.05 0.05 0.44 0.55

Relative Variance in: AER 1 2 3 4 5 10

2.45 2.36 7.64 7.83 8.27 8.75

11.67 31.36 34.72 34.44 34.18 35.13

45.64 39.46 36.67 36.38 36.03 35.13

0.05 0.63 0.81 1.41 1.45 1.51

40.18 26.20 20.16 19.94 20.07 19.49

Note: [See Table 4A for decompositions.]

0.00

550

A.M.M.

M a s i h a n d R. M a s i h

T a b l e 5A: D e c o m p o s i t i o n o f V a r i a n c e for M 2 M o d e l ( T h a i l a n d )

Percentage of forecast varianee explained by innovations in: Years Relative variance in: A Y 1 2 3 4 5 10

AY

AM2

AIR

ACP

AER

100.00 90.55 80.65 67.46 66.66 64.54

0.00 0.01 3.10 3.62 3.81 3.83

0.00 0.85 6.05 7.02 6.97 7.88

0.00 0.01 1.62 4.10 4.86 4.82

0.00 8.59 8.58 17.81 17.70 18.93

Relative variance in: A M 2 l 2 3 4 5 10

0.62 0,62 4,37 4.66 4.56 4.99

99.38 80.65 73.10 70.90 69.63 68.06

0.00 16.15 19.95 20.13 19.81 20.66

0.00 1.59 1.46 2.61 4.03 4.15

0.00 0.98 1.12 1.69 1.97 2.15

Relative variance in: A I R 1 2 3 4 5 10

0.06 10.38 11.88 11.00 11.41 11.60

14.70 12,01 11,49 11.28 11.05 10.85

85.24 77.46 73.13 68.36 67.96 67.59

0.00 0.02 2.69 6.88 7.16 7.20

0.00 0.12 0.80 2.49 2.42 2.75

Relative variance in: A C P 1 2 3 4 5 10

1.18 5.61 8.33 8.02 9.48 10.13

0.41 0.32 0.60 0.57 0.80 0.86

31.20 27.90 38.66 38.49 39.30 39.55

67.21 66.15 49.48 48.02 45.70 42.77

0.00 0.01 2.95 4.90 4.72 4.68

Relative variance in: AER 1 2 3 4 5 10

1.06 1.22 3.85 3.89 3.90 4. l 1

0.13 3.11 3.45 3.70 3.94 3.95

0.31 5.88 10.65 10.64 10.57 11.01

29.70 26.84 25.55 25.45 25.62 25.45

68.79 62.65 56.54 56.32 55.98 55.48

Notes: See Table 4A.

D Y N A M I C C A U S A L C H A I N IN M A C R O E C O N O M I C

ACTIVITY

551

T a b l e 5B: D e c o m p o s i t i o n o f V a r i a n c e for M 2 M o d e l (Malaysia)

Percentage of Forecast Variance Explained by Innovations in: Years

AY

AM2

A/R

A CP

AER

100.00 75.99 75.56 75.52 75.47 75.47

0.00 0.50 0.70 0.73 0.74 0.74

0.00 11.49 11.71 11.73 11.74 11.74

0.00 12.01 11.95 11.94 11.96 11.96

0.00 0.00 0.07 0.07 0.08 0.08

Relative Variance in: AM2 1 2 3 4 5 10

8.07 7.01 8.63 8.51 8.56 8.56

91.93 78.71 76.62 75.68 75.52 75.48

0.00 0.05 0.84 1.09 1.09 1.10

0.00 5.67 5.61 6.24 6.34 6.37

0.00 8.56 8.30 8.48 8.48 8.49

Relative Variance in: A I R 1 2 3 4 5 10

0.62 7.52 7.84 7.87 7.88 7.89

0.69 11.31 10.79 11.34 11.33 11.35

98.69 77.34 74.24 73.50 73.37 73.33

0.00 1.80 4.64 4.73 4.82 4.83

0.00 2.03 2.48 2.55 2.60 2.60

Relative Variance in: A C P l 2 3 4 5 10

1.39 8.97 10.02 9.97 9.99 9.99

0.05 14.12 13.45 14.00 13.97 14.00

7.44 5.88 5.96 6.03 6.02 6.03

91.12 68.97 68.07 67.38 67.36 67.32

0.00 2.06 2.50 2.61 2.64 2.65

Relative Variance in: A E R l 2 3 4 5 10

1.80 3.79 3.43 4.20 4.18 4.20

12.92 25.02 26.72 26.81 26.90 26.89

5.51 4.32 3.90 3.88 3.94 3.94

14.20 16.29 17.94 17.84 17.98 18.00

65.56 50.58 48.00 47.28 47.00 46.96

Relative Variance in: A y 1 2 3 4 5 10

Notes: See Table 4A.

552

A . M . M . Masih and R. Masih 0.02

0.015

n,=~.~

_ -- \ \

0.01 o I 0.005

/

,'

~ /

,"

i°

,

i /

'

"

/

~t,. i

~,\l

•

/'

"---_ \,

.

i

r--

-0.005

~i-,"

......... "" ""-: " ~ " " " "--I

-7:-----t-

I

-0.01

/

Pdces

-0.015

A

Years After Shock

0.01 o,ooa

1

/\P~

,

i o n | \

°°°i ...0.004J~'

:-'/- - - / 2 ~ \

7:/..,-.:,

. g

ExchangeRale

-0.006 B

YearsAfletShodc

Figure 1. Impulse responses of real output, prices, exchange rate, and interest rate from a one-standard deviation shock to money (MI) in (A) Malaysia and (B) Thailand.

as a percentage of GDP averaged 1.9 percent, which is the lowest in Southeast Asia, and the exchange rate remained more or less stable. Similarly, in the case of Malaysia, her average inflation rate for the same period was even lower (only 4.7°/o), and the exchange rate was also relatively stable. Although the budget deficit as a percentage of GDP was relatively high, it did not generate inflationary spiral because the deficit was not financed mainly through the printing press, and the monetary policy was primarily countercyclical and conservative like that in Thailand. Compared to the industrially and financially advanced economies, in these developing economies, such as Thailand and Malaysia, there are relatively more rigidities in wages and prices, and

DYNAMIC CAUSAL CHAIN IN MACROECONOMIC ACTIVITY

553

0.03 Inter~t

0.025 0.02

Rata,,

/

0.015

.7.

outptd

0.01 ,~ 0.005 ° _

2

~ -0.005

,

3

- --'~

7

8

°

9

41.01

".

-0.015 -0.02

, • -

Prices Exchange Rate

-0.025

A

Y.~,, AlterShock 0.00~

o.o~ Jr 0001 |-L "

,'" " " .471~"'°" ,'

,'L.

'

/N, ~ "

~'~" RealOulput~,,~.. j "'--"'-

22 / = =t

Interest Rate

B

Years After Shock

Figure 2. Impulse responses of real output, prices, exchange rate, and interest rate from a one-standard deviation shock to money (M2) in (A) Malaysia and (B) Thailand.

more imperfections of markets. The perfect and instantaneous adjustments (required by the New Classical paradigm) are conspicuous by their absence because of institutional and structural rigidities. Imperfections also stem from costly and imperfect information and delays in the transmission of information to the economic agents. In these circumstances, given the overall environment of a relatively stable macroeconomic balance as well as the stability and continuation of the major economic policies of the government (regardless of the change of governments) during most of the sample period, implying thereby an expectation-augmented supply curve not being completely vertical, it is not surprising that a monetary expansion (especially M1) was not necessarily dissipated merely in terms of higher nominal variables (such as prices, exchange rates,

554

A.M.M.

Masih and R. Masih

or interest rates) but rather contributed positively to help achieve an impressive rate of economic growth for both these economies. In this type of scenario, quite in line with expectations, an increase in money supply (particularly M 1) appears to have played the leading role of a policy variable being the most exogenous of all, and the other variables including output, rate of interest, exchange rate, and prices appear to have borne the burden of short-run adjustment in different proportions in order to re-establish the long-term equilibrium. ~2 The Granger-causal chain implied by our evidence that money supply (particularly M 1) more often predominantly led (rather than lagged) real output and the other three endogenous variables, appears to be consistent more with the Keynesian (in the case of Thailand) and the Monetarist (in the case of Malaysia) than with the recent Real Business Cycle School's macroeconomic paradigm.

4. SUMMARY, CONCLUSIONS, AND POLICY I M P L I C A T I O N S The main purpose of this paper is to discern the causal relations h i p - in the Granger (temporal) sense rather than in the structural s e n s e - a m o n g real output, money, interest rate, inflation, and exchange rate in the context of two small developing Southeast Asian economies, such as Thailand and Malaysia. The methodology employed uses various unit root tests and Johansen's cointegration test followed by vector error-correction model, variance decompositions, and impulse response functions in order to capture both

121n support of our contention, for instance see, inter alia, (in the case of Malaysia) Bank Negara Malaysia, AnnualReport (various issues), Government of Malaysia, Economic Report (various issues), and for a good summary of the monetary policy during the period under review, see Bank Negara Malaysia (1989). In the case of Thailand, see Bank of Thailand, Annual Economic Report (various issues), Bank of Thailand (1992), and P.G. Warr (Ed.), The Thai Economy in Transition (1993), in particular the papers by P.G. Warr on the Thai economy, N. Bhanupong on the monetary policy, and S. Chaipat on the state of fiscal policy. Finally, for both these countries, Asian Development Bank, Asian Development Outlook (annual). That a relative macroeconomic discipline is essential for rapid economic growth is also corroborated by Fry et al. (1988), who after reviewing the experiences of a large number of Pacific Basin developing countries, maintain that an empirical analysis of the rate of economic growth of these countries indicates that accomodative monetary and fiscal policies have negative effects on economic growth over the long run. Monetary accommodation of exogenous shocks adds noise to the economic environment by increasing the variances of money growth shocks.

DYNAMICCAUSALCHAIN IN MACROECONOMICACTIVITY

555

within-sample and out-of-sample Granger causality among macroeconomic activity. The broad policy implications from this analysis are varied but offer quite clear messages from a methodological and economic viewpoint. It is worth highlighting that this study made an initial attempt at placing the empirically controversial issue of causality between macro-aggregates in a temporal multivariate and cointegrated Granger-causal framework in the context of two Southeast Asian developing economies such as Thailand and Malaysia. Specifically, this study holds import for policy designers with respect to: 1. The evidence of cointegration among these variables tends to suggest that these five macro-aggregates are bound together by common trends or long-term equilibrium relationship(s). This implies that although these (cointegrated) variables will have short-term or transitory deviations (or departures) from their long-term common trend(s), eventually forces will be set in motion that will drive them together again. Moreover, evidence of cointegration rules out the possibility of the estimated relationship being spurious and implies that Granger causality must exist among these variables in at least one direction either unidirectional or bidirectional. This finding of cointegration or long-run equilibrium relationship among all these variables is very important for the policy designers. 2. The Granger-causal chain implied by three tools of our dynamic analysis (vector error-correction modeling, variance decompositions, and impulse response functions) implies valuable information regarding the lead-lag relationship between these macro-aggregates. Intuitively, given the relatively stable macroeconomic environment in these two growth-oriented economies, the results, quite in line with our expectations, tend to suggest that in the Granger-causality sense, money supply (particularly M1) appears to have played the leading role of a policy variable being the most exogenous of all, and the other variables including output, rate of interest, exchange rate, and prices appear to have borne most of the brunt of short-run adjustment endogenously in different proportions in order to re-establish the long-run equilibrium. The Grangercausal chain, implied by our evidence, that money supply (particularly M1) more often predominantly leads (rather than lags) output and the other three endogenous variables,

556

Masih and R. Masih

A.M.M.

is consistent more with the Keynesian (in the case of Thailand) and the Monetarist (in the case of Malaysia) than with the recent Real Business Cycle macroeconomic paradigm. This finding has clear policy implications in the sense that, as long as there is stability and continuation of economic policies (regardless of change of governments) within the framework of a proper macroeconomic discipline (implying thereby an expectation-augmented supply curve being not completely vertical), a monetary expansion in a small developing economy will not necessarily be dissipated merely in terms of higher nominal variables (such as prices, exchange rates, or interest rates) but will contribute positively to assist in achieving an impressive rate of economic growth as happened in both Thailand and Malaysia for the major part of the period under review. APPENDIX: TABLE A1 Unit Root Testing Procedures Statistic

Null hypothesis

Test equation

Reference/ Source Dickey/Fuller

ADF(x~)

A r , = IX + 8 T + u.x,_~

ADF(x~)

+ ~ y i A X t - i + et i=l m A r t = Ix + t l x t - ! + ~,,yitXSt-i + 5, i=l

ct = 0

a = 0

Dickey/Fuller

DP[t;,.(3)]

A3xt = Ix + (1AZxt-I + F.t

a = 0

Dickey/ Pantula

DP[t).,(3)]

A3xt = Ix + aA2xt-~ + ~ A r t - t + 5,

u = 0,

Dickey/ Pantula

m

(1981)

(1981)

(1987)

B=0

(1987) DP[t~.,(3)]

A3X, = Ix + a a 2 x t - i

+ 13Ax,-,

+ TX,-I + ~-t Z(ts.)

A r , = IX* + 8 * T + (~t* -

Z(O2)

A x t = IX* + 8 * T + (Q* + 8t

1)xt-i

Z(~3)

Axt = Ix* + 8 * T + (Q* -

l)xt-i

+

1)x,-i

a = 0, 13 = 0, y=0 8" = 1

Z(ot*)

A r t = Ix* + 8 * T + (a* + 5,

1)x,-~

Z(ta.)

Axt = Ix* + 8 * T + (¢t* -

1)xt I

0t* =

+

(1987)

Perron (1988)

8t

Ix* = 8" = 0 and a * = 1 8" = 0 and it* = 1 or*= l

+

Dickey/ Pantula

I~t

E:t

1

Perron (1988)

Perron (1988) Perron (1988) Perron (1988)

D Y N A M I C C A U S A L C H A I N IN M A C R O E C O N O M I C A C T I V I T Y

Z(a) Z(~t)

Axt = I~ + (ix - l)X,-j + e~ Art = I~ + (ct - l)X~-t + ~, Art = I~ + (a - 1)xt-i + ~,

J(P,q)

xt = ~a~T ~ + et and xt

Z(ta)

p

i=o q

= ~a~T i +

~t

557

a = 1 0t = 1 I~ = 0, a= 1 (RSS r RSSq)/ RSSq

Perron (1988) Perron (1988) Perron (1988)

p = 1

Sims (1988)

Park/Choi (1988)

i=o

7~ and a

(x - g) = p(xt-i - g) + e~

Notes: In the case of the Park-Choi test J(p, q), presented above is the actual test statistic. All tests listed above were conducted although results of simple Dickey-Fuller DF(x0, DF(T~), sequential Dickey-Pantula, Park-Choi, Sims, etc., tests are not presented in tables appearing in the main text. Results of these tests tended to confirm our findings from other test procedures; all results and regression details from all test equations are available upon request from the authors.

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