# TEMPORAL CAUSAL RELATIONSHIP BETWEEN STOCK MARKET CAPITALIZATION, TRADE OPENNESS AND REAL GDP: EVIDENCE FROM THAILAND

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3 Temporal Causal Relationship between Stock Market Capitalization MATERIALS AND METHODS The dataset used in this study comprises quarterly data during 1993 and Nominal GDP, real GDP at 1998 prices, exports, imports, and consumer price index are obtained from Thailand s National Economic and Social Development Board. GDP, exports and imports are in billions of baht. The series of stock market capitalization expressed in billions of baht is obtained from the Stock Exchange of Thailand website. Real stock market capitalization is obtained by deflating nominal market capitalization with consumer price index. Trade openness is simply the share of the sum of exports and imports in nominal GDP. All series are transformed into logarithmic series. The sample size comprises 84 observations. The present study adopts the asymptotic theory proposed by Pesaran et al. (2001) to test for the existence of level relationship between a variable and its regressors when the degree of integration of each variable is not certainly known. This bounds testing procedure can provide unbiased long-run estimates and valid test statistics. The unrestricted error correction models of this ARDL procedure can be expressed as: p1 p2 p3 lmc a a lmc a ly a lto lmc ly lto t t 1 12 t 1 13 t 1 1i t i 1 j t j 1k t k 1t q1 q2 q3 lto a a lto a ly a lmc lto ly lmc (2) t t 1 22 t 1 23 t 1 2i t i 2 j t j 3k t k 2t (1) r1 r2 r 3 ly a a ly a lmc a lto ly lmc lto (3) t t 1 32 t 1 33 t 1 3i t i 3 j t j 3k t k 3t where denotes first difference operator, lmc is the log of real stock market capitalization, ly is the log of real GDP, and lto is the log of trade openness. There are two steps in the bounds testing for cointegration. The first step is to estimate equations (1) (3) using ordinary least squares method to determine the existence of a long-run relationship between the three variables. This is done by conducting an F-test for the joint significance of the coefficients of lagged level variables. The null hypotheis H0 : ai1 ai 2 ai3 0, i 1, 2,3 is tested against the alternative hypothesis Ha : ai1 ai2 ai3 0, i 1,2,3. In other words, the models in equations (1) (3) are tested against the models without lagged level variables, which are the ARDL models, to obtain the computed F-statistic. If cointegration exists, the computed F-statistic will be larger than the upper bound critical value. If cointegration does not exist, the computed F-statistic will be smaller than the lower bound critical value. The computed F-statistic that takes the value between

4 1528 Komain Jiranyakul the upper bound and lower bound critical values will lead to an inconclusive result. The existence of cointegration gives the error correction mechanism (ECM) expressed as: p1 p2 p3 lmc a ETC lmc ly lto u (4) t i t i 1 j t j 1k t k 1t q1 q 2 q3 lto a ETC lto ly lmc u (5) t i t i 2 j t j 2k t k 2t r 1 r 2 r 3 ly a ETC ly lmc lto u (6) t i t i 3 j t j 3k t k 3t where ETC is the error correction term, which is the one-period lag of residuals obtained from the ordinary least squares estimate of level relationship between the three variables. The coefficient is the speed of adjustment toward the longrun equilibrium. The models in equations (4) (6) depict short-run dynamics of i each long-run equation and show how fast any deviation from the long-run equilibrium will be corrected. The main advantage of the conditional ARDL procedure in testing for cointegration is that re-parameterization of the model into the equivalent vector error correction model is not required compared with other techniques of cointeration analysis. The ECM representations expressed in equations (4) (6) show short-run relationship between changes in levels of the three variables and their lags, but they do not obviously exhibit short-run causality in the sense of Granger (1969) causality test. To test for the directions of short-run causations between the three variables, one can use a vector autoregression (VAR) model performed on stationary series (their first differences) to detect causations between stationary variables. The VAR representation can be expressed as: p p p lmc lmc ly lto v (7) t 10 1i t i 1i t i 1i t i 1t i 1 i 1 i 1 p p p lto lto ly lmc v (8) t 20 2i t i 2i t i 2i t i 2t i 1 i 1 i 1 p p p ly ly lmc lto v (9) t 30 3i t i 3i t j 3i t i 3t i 1 i 1 i 1

5 Temporal Causal Relationship between Stock Market Capitalization The optimal lag p can be determined by Akaike information criterion (AIC). If cointegration exists, the VAR representation can be augmented with the ECT of long-run relationship. In this case, both short-run and long-run causality can be tested by using Wald F-test. If cointegration does not exist, the VAR model will not be augmented with the ECT, and thus only short-run causality can be tests (see Granger, 1988). 3. EMPIRICAL RESULTS In testing for cointegration using the ARDL approach mentioned in the previous section, testing for unit root of series in questions is not required. However, this approach is not suitable if any series is integrated of order two, i. e., it is I(2) series. According to Choi and Chung (1995), the more powerful test for relatively small sample size is the PP tests proposed by Phillips and Perron (1998). The results of unit root tests are reported in Table 1. The results in Table 1 show that market capitalization series is integrated of order one, I(1), while the series of real GDP and trade openness seem to be either I(0) or I(1) series. The tests in first differences of all series show that the order of integration does not exceed one. Therefore, The ARDL procedure is suitable for cointegration test. Table 1 Results of PP tests for all variables: 1993Q1-2013Q4 Level of variables First difference of variables Variables Test A Test B Test A Test B Integration lmc [3] [3] [2] [2] I(1) (Market cap.) (0.63) (0.75) (0.00)*** (0.00)*** ly [35] [6] [44] 17.05[44] I(1) or I(0) (real GDP) (0.85) (0.03)** (0.00)*** (0.00)*** lto [27] [5] [81] [61] I(1) or I(0) (Trade openness) (0.25) (0.03)** (0.00)*** (0.00)*** Note: Test A includes intercept only while Test B includes intercept and a linear trend. The number in bracket is the optimal bandwidth. ***, ** and ** denote significance at the 1, 5 and 10 percent level, respectively. The number in parenthesis is the probability of accepting the null hypothesis of unit root. I(1) or I(0) indicates that at least one test shows the series is I(0). The models in equations (1) (3) are used for testing the existence of level relationship between stock market capitalization, trade openness and real GDP using parsimonious models. The results from bounds testing for cointegration are shown in Table 2.

6 1530 Komain Jiranyakul Table 2 Results from Bounds testing for cointegration: 1993Q1-2013Q4 2 Computed F ARDL model (2) (1) lmc, ly, lto 1.53 (2,1,1) (p=0.678) (2) lto, ly, lmc 1.63 (2,1,1) (p=0.082) (3) ly, lmc, lto 6.25 (2,1,0) (p=0.845) Note: The LM test for serial correlation in the specified ARDL models is represented by 2(2). Three variables: lmc, ly and lto denote market capitalization, real GDP and trade openness, respectively. The results from bounds tests indicate that cointegration exists only in Model 3 with real GDP as the dependent variable. The computed F-statistic of 6.25 is greater than the critical value of 4.85 at the 5 percent level of significance (Table CI (iii) Case III in Pesaran et al., 2001). The other two models with market capitalization and trade openness as the dependent variable give the computed F-statistics that are smaller than the lower bounds critical value at the 10 percent level of significance. Since the ARDL(2,1,0) model does not exhibit serial correlation as demonstrated by of the LM test, the long-run relationship and short-run dynamics are 2(2) estimated. The results are shown in Table 3. Panel A of Table 3 shows the estimate of long-run relationship between real GDP, market capitalization and trade openness. The dummy variable of the 1997 financial crisis is not included because it distorts the results. As a matter of fact, the crisis could cause fluctuations in real effective exchange rate, which in turn could affect exports and imports, and thus the impact of trade openness on real GDP can be distorted. It is apparent that stock market capitalization and trade openness exert the positive impacts on real GDP in the long run. A one percent increase in stock market capitalization causes a 0.20 percent increase in real GDP. This result confirms the findings by Demirguc-Kunt and Levine (1996) and Shahbaz (2012). Similarly, a one percent increase in trade openness causes a 0.52 percent increase in real GDP. This finding does not support the evidence provided by Sarkar (2008), Lloyd and MacLaren (2000), but confirms the results of Yanikkaya (2003). Panel B of Table 3 shows the estimate of the short-run dynamics from the ECM representation. The relationship between a change in stock market capitalization and economic growth is significantly positive while the relationship between a change in trade openness and economic growth is significantly negative. However, the sizes of these coefficients are minimal. In addition, one-period lagged change in stock market capitalization has a small impact on economic growth. The significance of lagged

7 Temporal Causal Relationship between Stock Market Capitalization Table 3 Results of long-run and short-run dynamics estimates of the impact of stock market capitalization and trade openness on real GDP, 1993Q1-2013Q14 Panel A. Long-run estimation with ly t as dependent variable Coefficient lmc t (10.515)*** lto t (11.548)*** Constant (13.052)*** Adjusted R Panel B. ECM estimation with Äly t as dependent variable ly t (-1.757) ly t (-4.687)*** lmc t (2.206)** lmc t (1.824)* lto t (-1.917)* ECT (-3.932)*** Adjusted R Diagnostic tests: Functional form: (p=0.025) Serieal correlation (LM): (p=0.077) 2(2) Normality of residuals: JB (p=0.000) Heteroskedasticity: ARCH(1) (p=0.269) Note: The number in parenthesis is t-statistic. p is the probability of accepting the null hypotheses that there is no serial correlation, no heteroskedasticity in the residuals, the residuals are normally distributed, and correct specification of the functional form. ***, ** and ** denote significance at the 1, 5 and 10 percent level, respectively. Three variables: lmc, ly and lto denote market capitalization, real GDP and trade openness, respectively. change in market capitalization justifies the choice of selected lag length. The estimated conditional ECM equation fails to pass the functional form misspecification test at the 5 percent level of significance. This indicates that there might be some asymmetries or non-linear effects in the adjustment of real GDP process, which a linear specification cannot take into account. Furthermore, the presence of non-normality in the residuals might be due to a small or moderate sample size. The inferences about the estimated coefficients in terms of F-tests and t-tests should be reasonably accurate because the variance of the residuals is constant, which is confirmed by the ARCH test. Overall, the estimated ECM equation has some desirable features. The highly significant coefficient of the ECT is minus and has the absolute value of less than one. This indicates that any deviation from long-run equilibrium will be rapidly corrected. The results of short-run and long-run causality among variables are reported in Table 4.

8 1532 Komain Jiranyakul Table 4 Results of Granger causality tests Dependent Short-run causality Long-run variable causality lmc to ly ECT lmc [-] [-] - (0.444) (0.000) lto [+] ** [-] - (0.931) (0.017) ly [+] 5.095***[-] *** (0.267) (0.000) (0.000) Note: The optimal lag length of seven is determined by AIC. The Wald F-statistic is reported with the probability of accepting the hull hypothesis. [+] and [-] indicate positive and negative causation, repectively. *** and ** denote significance at the 1 and 5 percent level. The Wald coefficient restriction tests identify the short-run causations between the three variables when cointegration does not exist in Models 1 and 2 as reported in Table 2. In Model 3, the existence of cointegration allows for testing both shortrun and long-run causations. The results show that there is unidirectional causality running from market capitalization and trade openness to output growth in the long run. In the short run, market capitalization imposes insignificantly positive impact on output growth, trade openness imposes significantly negative impact on it. Furthermore, output growth significantly imposes negative affect on trade openness. Therefore, bidirectional causations between trade openness and output growth are observed in the short run. This finding is similar to the finding by Birinci (2013), but with the opposite sign of causations. Stock market capitalization is not affected by both trade openness and output growth as evidenced by the Wald F-statistic. The Granger causality/block exogeneity test is also conducted to examine which variables are exogenous in the model. The optimal lags of seven are determined by AIC. The results are shown in Table 5. Table 5 Results of VAR Granger causality/block exogeneity Wald test Dependent variable lmc lto ly 2 for joint test (7) (0.381) (0.077)* (0.000)*** Note: The number in parenthesis is p-value. ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. Three variables: lmc, ly and lto denote market capitalization, real GDP and trade openness, respectively.

10 1534 Komain Jiranyakul Choi, I., Chung, B., (1995), Sampling frequency and the power of tests for a unit root: a simulation study, Economics Letters, 2(3), Demirguc-Kunt, A., Levine, R., (1996), Stock markets, corporate finance and economic growth: an overview, World Bank Economic Review, 10(2), Granger, C. W. J., (1969), Investigating causal relations by econometric models and cross spectral methods, Econometrica, 37(3), Granger, C. W. J., (1988), Causality, cointegration, and control, Journal of Economic Dynamics and Control, 12(2-3), Law, S. H., Demetriades, P. O., (2004), Capital inflow, trade openness and financial development in developing countries, Money Macro and Finance (MMF) Research Group Conference Paper. Liu, W. C., Hsu, C., (2006), The role of financial development in economic growth: the experiences of Taiwan, Korea, and Japan, Journal of Asian Economics, 17(4), Lloyd, P. J., MacLaren, D., (2000), Openness and growth in East Asia after the Asian crisis, Journal of Asian Economics, 11(1), Pesaran, M. H., Shin, Y, Smith, R. J., (2001), Bounds testing approaches to the analysis of level relationships, Journal of Applied Econometrics, 16(3), Phillips, P. C. B., Perron, P., (1988), Testing for a unit root in time series regression, Biometrika, 75(2), Sarkar, P., (2008), Trade openness and growth: is there any link?, Journal of Economic Issues, 42(3), Shahbaz, M., (2012), Does trade openness affect long-run growth?: cointegration, causality and forecast error variance decomposition tests for Pakistan, Economic Modelling, 29(6), Tsen, W. H., (2006), Granger causality tests among openness to international trade, human capital accumulation and economic growth in China, , International Economic Journal, 20(3), Yanikkaya, H., (2003), Trade openness and economic growth: a cross-country empirical investigation, Journal of Development Economics, 72(1),

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