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ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS JANURAY 2011 VOL 2, NO 9 Two steps are involved to implementing this procedure. In the first step optimum lag length (k) of VAR and the maximum order of integration (dmax) of the variables in the system is determined. Then a level VAR is estimated with a total of k+dmax lags. In the second step a standard Wald tests is applied to the first k VAR coefficient matrix to make Granger causal inference. In order to test for Toda and Yamamoto (1995) based Granger causality between core variables (LnNYt, LnHt, LnMXt, LnSMXt and LnPXt) we estimate the following VAR(k+dmax) model: � LnNYt � �C1 � � � � � � LnHt � � C2� � LnMX t � � �C3 � � � � � � �LnSMX t � �C4 � � � LnPX � � � � � t C5 � k �d max � i�1 ��1i � � �2i ��3i � �� 4i � ��5i � � � � � 1i 2i 3i 4i 5i � � � � � 1i 2i 3i 4i 5i � � � � � 1i 2i 3i 4i 5i �1i � � LnNYt �i � �� 8t � � � � � � � 2i � � LnHt �i � � �9t � …… (12) �3i � � LnMX t �i � � ��10t � � � � � � �4i � �LnSMX t �i � ��11t � � � � � � � � � � � 5i LnPX t �i �12t � where C‘s are the intercepts. θ‘s, β‘s, η‘s, ρ‘s and λ‘s are the coefficients of LnNYt, LnHt, LnMXt, LnSMXt and LnPXt respectively. �‟s are error terms that are assumed to be white noise. Having estimated above system of equations, usual Wald tests are then applied to the first k coefficient matrices using the standard χ 2 -statistics. The hypothesis can be drawn as LnNYt ―Granger-causes‖ LnHt if β1i ≠ 0, LnMXt if η1i ≠ 0, LnSMXt if ρ1i ≠ 0 and LnPXt if λ1i ≠ 0. Similarly, other hypothesis can be drawn for unidirectional and bidirectional causality among rest of the under investigating variables. 5. Empirical Results Before testing for cointegration for the analysis of short and long run dynamics, the unit root test is used in order to investigate the stationarity properties of data and all variables are found stationary at first difference. After investigating the order of integration, the study proceeds for cointegration, parsimonious ECM Model and Granger causality test. For the investigation of dynamic behavior of all variables, impulse response function and variance decomposition is also carried out. 5.1 Order of Integration ADF unit root test is employed to test for the stationarity in logarithmic form of NYt, Kt, Lt, Ht, Xt, MXt, SMXt, PXt and CMt at level and then first difference of each series. The results of the ADF test at level and first difference are reported in Table 1, by taking into consideration trend variable and without trend variable in the regression. Based on Table 1 at level, the tstatistics for all series from ADF test are statistically insignificant to reject the null hypothesis of non-stationary at 5% significance level. This indicates that these series are non-stationary at their level form. Therefore, these variables have a unit root problem or they share common stochastic movements. COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 452
ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS Table 1: Dickey-Fuller (DF)/Augmented Dickey-Fuller (ADF) Test for Unit Root JANURAY 2011 VOL 2, NO 9 Augmented Dickey-Fuller (ADF) Test Variables at Level Variables at 1 st Difference Without Trend & Without Trend & Trend (k) Intercept (k) Trend (k) Intercept (k) Ln NYt -1.619 (0) -2.202 (0) -4.554* (1) -5.375* (1) Ln CFt -1.679 (0) -1.736 (0) -4.542* (1) -5.535* (1) Ln ELFt -0.502 (0) -2.407 (0) -6.037* (0) -5.987* (0) Ln HCt -0.129 (0) -1.692 (0) -4.627* (0) -4.572* (0) Ln MXt -1.9015(0) -2.057 (0) -5.348* (0) -5.879* (0) Ln SMXt -1.997 (0) -2.382 (0) -5.525* (0) -5.575* (0) Ln PXt -1.889 (0) -2.231 (0) -5.777* (1) -5.683* (1) Ln CMt -1.156 (0) -2.042 (0) -5.558* (0) -5.836* (0) Notes: The null hypothesis is that the series is non-stationary, or contains a unit root. The rejection of null hypothesis for ADF test is based on the MacKinnon critical values and numbers in parentheses indicate number of lags (k) based on Schwarz Information Criterion (SIC). * Indicates the rejection of the null hypothesis of non-stationary at 5% significance level. When the ADF test is conducted at first difference of each variable, the null hypothesis of non-stationary is easily rejected at 5% significance level as shown in Table 1. This is consistent with some previous studies 4 which demonstrated that most of the macroeconomics and financial series are expected to contain unit root and thus integrated of order one, I(1). Therefore, variables are integrated of order one, and a higher order of differencing is not required to perform. Since the variables are integrated of same order, I(1) , so, multivariate cointegration test for long run analysis could be conducted. 5.2 Empirical Results for Short and Long Run Dynamics 5.2.1 Long Run Dynamics The results of the Johansen‘s cointegration test for Net Income, Physical Capital, Labor, Human Capital and components of total Exports (Manufacturing exports, semimanufacturing exports and primary exports) using both the �trace and the � max statistics are given in Table 2. Starting with the � max test results reported in Table 2, the null hypotheses r = 0 and r � 1 (no cointegration) can be rejected in favor of r = 1 and r = 2 respectively, indicates the presence of two cointegrating relationships between NYt, Kt, Lt, Ht, CMt, MXt, SMXt, and PXt. For the trace test results, the null hypotheses r = 0, r � 1, r � 2, and r � 3 can be rejected in favor of r � 1, r � 2, r � 3 and r � 4 implies that there are at least four cointegrating vectors between NYt, Kt, Lt, Ht, CMt, MXt, SMXt, and PXt at 5% level of significance. 4 See for example Ahmad et al. (2004), Akbar .M and Z. F.Naqvi (2000), Ahmad at al. (2000) COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 453
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