Exploring Co-Integration and Causality between Foreign Trade and Economic Growth: Econometric Evidence from India

Foreign trade has played an important role in India‘s economic growth in the past two decades (Semi et. al., 2017; Ghoshal, 2015). It has enhanced competitiveness; expanded business opportunities for domestic markets. By removing unnecessary barriers, it made easier for India and the world to export and import. Since the economic reforms introduced in the year 1991, India has radically changed its trade relations with the world economies. The trade between India and the world economies has risen sharply in the present decade. The study examines the dynamics of the relationship between India‘s foreign trade and economic growth in India during 1991-91 to 2017-18. Dutt and Ghosh (1996), Nidugala (2000), Nataraj et al. (2001), Bhattacharya and Bhattacharya (2009), Ray, (2011), Kaur and Sidhu (2011) and Agrawal (2014) highlighted that the Gross Domestic Product (GDP) is used as the proxy for economic growth in India. In the present study, an attempt has been made to analyze the data collected through Secondary source. The results are divided into two sections. The first section (i) is devoted towards the estimates of average relationship India‘s GDP and Foreign Trade. The second section (2) discusses the extent of in order to analyze the stationarity of India‘s Foreign Trade and GDP and in this section also find out the long run and short run relationship between Foreign Trade and GDP.

The study is based on the availability of secondary data which collected from different sources such as Direction of trade Statistics, IMF, World Bank,, Asian Development Bank, Handbook of RBI, UNCOMTRADE.

To explores the association of India‘s GDP (IGDP) and Foreign Trade (FT) during 1991 to 2017 study used regression model. The study regress GDP on Trade and another Trade regress on GDP. The regression equations (1 and 2) of GDP on Trade and Trade and GDP show the average relationship between GDP and Foreign Trade. The results are obtained from the statistical Eview 10. The study also checks the Foreign Trade and GDP time series data are stationary or not. To analysis the time series data are stationary or not, study uses the Unit Root Test. Unit Root Test explains with the help of Augmented Dickey-Fuller (ADF) Test and Phillips-Perron (PP) Test at level and first difference. possibilities, the Dickey fuller test is estimated in the three forms, GDPt is a random Walk with drift around a stochastic trend: Here t is trend variable. H0: δ =0, There is Unit Root (The time series is nonstationary) H1: δ <0, the time series is stationary.

The Equation 6 and 7 are show the ADF intercept model and equation 8 and 9 are shows ADF trend and intercept model in case of GDP and FT. The ADF tests the null hypothesis (H0) against the alternative (H1) hypothesis; H0: Each variable has a unit root; H1: Each variable does not have a unit root

The Equation 10 and 11 are show the PP Test on GDP and FT. Coefficient from AR (1) regression to account for the serial correlation in εt. , H0 : = 0; H1 :

> 0

The present attempt explores the association of India‘s GDP and Foreign Trade during 1991 to 2017. The study regress GDP on FT and another FT regress on GDP. The regression equation (12) shows the average relationship between GDP and Foreign Trade. The results show that there is positive average relationship between GDP and FT. The intercept term is positive which shows that if FT is equal to zero then GDP is $ 215745.3 US Million Dollar. The study test the null hypothesis that there is no relationship between GDP and FT that is slope of coefficient β2 =0. The study reject the null hypothesis there is no relationship between GDP and FT. It is also shows that FT has significant effect on the GDP at five percent level. The regression equation (13) of FT on GDP shows show that there is positive average relationship between GDP and FT. The intercept term is negative i.e. if GDP is equal to zero then FT is negative $ -62141.87 US Million Dollar. It is also shows that GDP has significant effect on the FT at five percent level. In this section also study check the Foreign Trade and GDP time series data are stationary or not. In this section analyses the long run and short run relationship between Foreign Trade and GDP during 1991-92 to 2017-18. To analysis the time series data are stationary or not, study uses the Unit Root Test. Unit Root Test explains with the help of Augmented Dickey-Fuller (ADF) Test and Phillips-Perron (PP) Test at level and differences. To explore the long run relationship among GDP and FT, used the Johansen Cointegration Tests. The present section determining whether one time series is significant in forecasting another used, Granger causality test. To examine the long run relationship used the Error Correction Model.

(drift around a stochastic trend) (16) Std. Error (3476231) (6397.327) (0.071)

Std. Error (0.033) t- Statistic (1.392)

(drift around a stochastic trend) (19) Std. Error (31651.17) (4277.435) (0.116)

A random walk process may have no drift, or it may have drift or it may have both deterministic and stochastic trends. To allow for the various possibilities, the Dickey fuller test is study in the three forms. The equations (14; 15; 16; 17; 18 and 19) show that the time series of GDP and total trade are no stationary.

Phillip-Perron tests to identify stationary of the data. The results of the unit root test (ADF and PP test) are exhibited in table 1. The ADF and PP test are performed for two models; intercept as well as trend and intercept. Both models are performed on the level as well as d difference of the series. On the basis of the pure random walk model (i.e., intercept, trend) both the rates are not GDP is nonstationary time series. The GDP is stationary at I (2) in ADF intercept and trend + intercept l Model. The same type results found in case of PP intercept and trend + intercept model. The GDP is stationary at I (2) and I (1) in intercept and Trend + intercept PP model. The Indian foreign trade time series data is nonstationary in intercept ADF model and PP Model. The FT is stationary at I (1) in intercept ADF model; I(0) in intercept +trend ADF model and I(1) intercept and trend + intercept PP Model.

The next step is to search for co integrating relationships among the GDP and FT. Here the Johansen-Julesius test which provides the most efficient estimate of the Cointegrating significant vectors and also identifies the number of cointegrating relationships among the non-stationary variables is employed. In other words, the study has to examine whether or not there exists a long run relationship between variables (stable and non-spurious co-integrated relationship). In our case, the mission is to determine whether or not foreign trade (FT) and economic growth (GDP) variables have a long run relationship in a bivariate framework in table 2.

The λtrace Value and the λmax Value statistics for determining the number of cointegrating vectors (r) wherein the null hypothesis of no cointegration (r = 0) versus the alternatives of r >0 and r>1 have been tested. The results reported in Table reveal that the null hypothesis of no cointegration (r = 0) rejected at the 5 percent level of significance. But the null hypothesis that r ≤1 is accepted at the 5% level vectors show the long run equilibrium relationship among GDP and Foreign trade, the dynamic adjustments that occur in the short run leading to stable long-run relations in response to various shocks to the system remain unspecified. The normalized cointegrating equation is

Granger causality test Granger causality test is a technique for determining whether one time series is significant in forecasting another (Granger. 1969; Granger and Newbold, 1974). FT and GDP are, in fact, interlinked and co-related through various channel. There is theoretical or empirical evidence given by Dutt and Ghosh (1996), Nataraj et al. (2001), Nidugala (2000) and Ray (2011) that could conclusively indicate sequencing from either direction. For this reason, the Granger Causality test was carried out on FT and GDP. Ray (2011) suggested the causality equation between GDP and FT. If causality (or causation) runs from FT to GDP, we have: If causality (or causation) runs from GDP to FT, it takes the form: The equation 21 and 22 show that GDP t and FTt represent gross domestic product and foreign trade respectively, εit is uncorrelated stationary random process, and subscript t denotes the time period. Therefore, it is assumed that the disturbance terms ε1t and ε2t are uncorrelated. In equation4, failing to reject: H0: b =0 implies that foreign trade does not Granger cause economic growth and alternative hypothesis that H1: b≠ 0. On the other hand, failing to reject H0: d=0 implies that economic growth via GDP growth does not Granger cause foreign trade.

The decision rule: ∆FTt-1Granger causes ∆GDPt, the coefficient of the lagged values of FT as a group (b) is significantly different from zero based on F-test (i.e., statistically significant). Similarly, ∆GDPt-1 Granger causes ∆FTt if d is statistically significant.

In the present section researchers study that GDP and FT are cointegrated; that is, there is a long term or equilibrium, relationship between the two. In the short run they may be disequilibrium (Sargan, 1984; Gujarti, 2005). ECM equation (23) sates that ∆GDP depends upon ∆FT and also on the equilibrium term. Where ∆ denotes the first difference operator, t is a random error term, ut-1 = GDPt-1 – β1 – β2FTt-1 is the one period lagged value of the error form the contegrating regression GDP= α + α1 FT + µi . Here ut-1 = 1 GDPt-1 - 2.284FTt-1 - 265856.978 is the one period lagged value of the error form the contegrating regression or it represents the long run equilibrium between GDP and FT. ECM equation (25) sates that ∆FT depends upon ∆GDP and also on the equilibrium term. Where ∆ denotes the first difference operator, t is a random error term, Ut-1 = FTt – β1 – β2GDPt-1 is the one period lagged value of the error form the contegrating regression FT = β+ β1 GDP + µi. Therefore, one can treat the error term in Ut= FTt – β1 – β2GDP as the equilibrium error.

Here ut-1 = 1 FTt-1- 0.437* GDPt-1+ 116394.683 is the one period lagged value of the error form the co integrating regression or it represents the long run equilibrium between GDP and FT.

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Assistant Professor, Department of Commerce, G. B. P. G. College, Rohtak