Description
Causality based export forecasting
1 CAUSALITY BASED EXPORT FORECASTING-FOR INDIAN ECONOMY Arti Omar2 This study is an attempt to identify future relationship of export with Index of Industrial production and Consumer price index by using Causality model. Export account for a significant proportion of Industrial value-added and employment due to the capability to innovate new products for commercial advantage and demonstrated capability to move export up the value-chain as wages rise. Future growth of export is totally depended on the future trade policies for import-export, fiscal policies, future foreign investment, and Industrial growth as well influenced by export prices in relation to world prices in other words inflationary and deflationary condition in India. *We thank to Dr. Pradip Swarnakar and Dr. Ajay kumar for their kind support during the study.
2
I.
INTRODUCTION
In India exports are growing everyday and you can access this vast market with various available data. India’s outstanding economic condition and its remarkable role in the global economy have lead to a great amount in further attention and research. India’s export has grown faster than the GDP over the past decades. GDP of any country helps in determining the growth rate and welfare of that country. GDP helps in determining the performance of any country. There have been many articles on the GDP increment of Indian economy; some of them said that the Gross Domestic Product (GDP) in India expanded 7.7 percent in the second quarter of 2011 over the previous quarter. The main factor that contributed to the Indian economy in 1990 was the opening of the Indian economy. From 2000 until 2011, India's average quarterly GDP Growth was 7.45 percent reaching an historical high of 11.80 percent in December of 2003 and a record low of 1.60 percent in December of 2002 [Trading Economics]. The export of any country is driven by factors like GDP and Industrial Production. Industrial Production is a very crucial factor in determining the growth of any country. Considering this fact we will be using GDP and Industrial Production as base and then accordingly determine the growth of the country. The factors which affect export are mainly the demand and the supply chain of any country. The demand chain of a country determines whether the export of that country is up to mark or not and the demand of any country is determined by the Industrial Production of that country. In a way all the three factors affect each other. After the foreign investment and free market openness factors, export has become the dominating factor to sustain economic growth of India. Export is one of the most fast growing sectors in India. As the export sector of Indian economy has always delineated impressive growth in all the areas of export. The engineering industry has been performing consistently over the years in the arena of exports as it secured the second position in terms of the earnings from exports in 2004-05, amounting to US $ 10516.45million, which increased to US $ 14587.37million in the next fiscal. The performance of textile industry has fluctuated a little as the earning of the textile industry from exports in the financial year 2004-05 was US $ 12204.71million which came down to US $ 12017.46 million in 2005-06. USA has turned out to be the most significant export partner of India and the export sector of Indian economy earned approximately US $ 13265.60 million in 2006-07. UAE has stood second only to USA as UAE contributed 9.7 out of the total Indian earnings from exports in 2006-07. UK and China has exchanged their positions in the current year as China's share among the exports figure in India in 2006-07 has improved by 6.3 % in comparison to 2005-06. In 2004-05 Belgium and Italy contributed substantially to the earnings from exports, with a contribution of US $ 2442.09 million [maps of India].
3 Here in our study we would be considering Industrial Production as our base to study the behavior of Indian export and use the data to forecast the future growth of Indian export theoretically. For computing the relation between the different variables we would be using co-integration model. Co-relation & Causality analysis of variables would tell us the relation between the various variables used in the study. The short run correlation between the variables helps to determine the relation and the direction of the relation among the variable, which would intern help in forecasting of export. The forecasting of the variable will help in predicting the future growth of that variable in India, and using this data India’s concerned authorities can take actions accordingly and this reduces risks of failures. So, objective of this study is to define long-run and short run relationship among stated variables (Export, Index of Industrial Production, and Consumer Price Index). This study is an attempt to find best fit solution of following hypothesis. 1. Does Export cause Industrial Production? 2. Does Industrial Production cause Export? 3. Does Export cause CPI? 4. Does CPI cause Export? 5. Does CPI cause Industrial Production? 6. Does Industrial Production cause CPI? 7. How export forecast is affected by Industrial production and CPI? In order to fulfill the research questions I have chosen quarterly data of Industrial Production, Export, and CPI in relatively same time frame. II. Literature Review
The world trade structure has been changing at a very rapid rate in the recent years. As the observation of the WTO report 2003 indicates following are the noteworthy features of the world trading system to emerge in the last few years. Firstly, the growth in the share of south-2 trade in the world trade owing largely to the growing liberalization of the trade and investment regimes of the developing countries. Secondly, it points to the decline and continued volatility of the commodity prices with factors such as trade policies in the developing countries, the structure of global international market for commodities and global macro-economic conditions, and lastly, the proliferation of regional trade agreements over the past decades or so, which have severe implication on the world trade structure, costs and standards (Rajesh Mehta, Parul Mathur [2003]).
4 India is moving at consistent rate towards establishing its presence in the world trade. In 2002, India ranked as the top 30th-leading exporter amongst the leading exporters in the world merchandise trade. Amongst the world’s leading importers in the world merchandise trade 2002, India ranked 24th (source: WTO). As indicated by India’s EXIM Mid Term Policy(2002-2007), India aims to have at least 1% share in the total global export from the level of the 0.71% in 2001-2002, implying a compound annual growth rate of 11.9% (in dollar terms) over the Tenth Five year Plan. 2002-2007. through the 1990’s while World trade value has increased 1.9 times, India’s export in U.S$ terms were up by 2.5 times. The percentage share of India’s exports in global exports remained more or less at around 0.52 percent during early 1009’s and showed a significant increase in 1995-96 to 0.60 percent remaining steady at around 0.63 thereafter till 1990. India’s export value sharply accelerated in 2000, from 35.66 U.S$ billion in 1991 to 43.3 U.S$ billion in 2001. But the year 2001 experienced the deepening and reinforcing of the global economic slowdown as depicted by the decline in the world trade form 6310.1 in 2001 to 6120.8 U.S$ billion in 2001. The prevailing global slowdown was accentuated further by the terrorist attack in the United States on December 11, 2001 which resulted in further downward growth projections for all most all major economic regions of the world. Increase in the value of India’s exports from 42.1 to 44.2 U.S$ billion. India’s share in global exports increased from 0.67 to 0.71 percent from 2000 to 2001. As the trends point out the year 2002 witnessed a substantial rise in India’s export with the growth rate of 18.6%. This led to a jump in the share of India’s exports from 0.71% to 0.80% in the global exports. From the phase of declining global exports and India’s export in the latter quarters of 2001, 2002 has been a phase of gradual but significant recovery (Rajesh Mehta, Parul Mathur [2003]). Goldstein and Khan (1985) studies was based on income and price effects in foreign trade. A beautiful combination of econometrics and trade modeling can be seen in this study, as well as this study employs the summary of various estimates of income elastic and related policy issues and price. Our first discussion will be based on recent studies have been done on export, that how export get affected by other macro-economic variables of a given country. According to Khan (1974) has investigated for the period 1951-1969 employing annual data for individual countries using the following model specification: (1). log MDit =a0 +a1 log
PMit PDit
This equation represents import demand function.
+a2 log Yit +Ut
5
domestic price level of country i, Yit is the real GNP of country i, Ut and is an error term associated with each observation. (2). log Xdit =b0 +b1 log(PX i /PWi )t =b2 log Wt + Vt
Where MDitis the quantity of imports of country i, PMit is the unit value of imports in country i, PDit is the
This equation represents export demand function
world price level, and W is the real world income. Since each variable is defined in logarithmic terms, the estimated coefficients are the elasticity of imports and exports with respect to the corresponding variables. These functions of import and export are estimated by using OLS (ordinary least square method). This study reveals that in developing countries price is the main factor that plays an important role. Secondly export as a demand function is described by Warner and Kreinin: (3). ln Xi =c+a1 ln YWi + a2 ln PXLCi + a3 lnEi + a4 ln EPi + a5 ln PFCcomp
Where Xdit is the quantity of exports of country i, PXi is the unit value of exports of country i, PWi is
rate, which is proxies by Ep =[0.7( log Et - log Et-1 )+0.3( log Et-1 - log Et-2 )]. PFCcomp is the average
effective exchange rate index of country I, (1975=100), EPi is the expected rate of change in the exchange export price of 64 competing countries expressed in foreign currencies, weighted by each competing country's exports into each of the markets. Having estimated the demand for imports and exports using OLS technique, the study of Warner and Kreinin employs that the introduction of floating exchange rates
where X i is the volume of the country's exports, YWi is the weighted average GDP of 23major importing countries facing country i, PXLCi is the export unit value index of the country i, 1974=100, Ei is the
appeared to have affected the volume of imports in several major countries, but the direction of change varied between them. In the competing countries exchange rate and the export price are found to be powerful determinants of a country's exports because the fact competing countries want to export to get foreign currency more in comparison to developed countries and developing countries. So exchange rate is an important factor whereas due to globalize competitive market competitive countries want to export the commodities under the range of economies of scale.
6 III. Data & Variables
This analysis selected three macro-economic variables such as Industrial Production (IP), Export (Exp), Consumer Price Index (CPI). GDP per capita National Income and Industrial production Index (IIP) are conversely used for economic growth. So, in this study we used the change in index of Industrial Production (IIP) for GDP growth rate. All three variables are converted into natural logarithm. All Analyses run with monthly data of all three variables over the period of 1994:4 through 2010:12. This monthly data set is obtained from OECD (Organization for Economic Co-operation and Development), Global finance Data, Reserve Bank of India, Asian Development Bank. IV. RESEARCH METHODOLOGY
With this study attempts are made to test and search for evidence of existence of relationship. This empirical analysis comprises of three parts. IV(i) UNIT ROOT TEST:
Under this section, study examines stationarity properties of data. For stationary series it is required series should have constant mean and variance through time and auto co-variance is not time varying for each given lag value (Enders, 2004). In stationary time series, shocks will be temporary and over the time their effects will be eliminated as the series revert to their long run mean values. Unlike stationary, nonstationary time series contains permanent components (Asteriou, 2007). Generally it is found that most of the economic variables show a trend and therefore most of the cases non-stationarity is found and these non-stationary time series can easily lead the Ordinary Least square (OLS) regression to incorrect or spurious conclusion. Thus, the key way to test for non-stationary is to test for existence for unit root or the order of integration (I) of variables. The present study employs the Augmented Dickey-Fuller (ADF, 1979, 1981), Phillips, and Perron (PP, 1988) test for unit root test. If all of the series are non-stationary in levels, it should be stationary in successively differences with the same level of lags. ADF test includes extra lagged terms of the dependent variables in order to eliminate auto-correlation. The lag length of this extra term is determined by the Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) Regression equation of ADF test takes following form: (4). ?Yt =?0 +?T+?Yt-1 + ?i=1 ?i ?Yt-1 +?t
p
The ADF regression test for existence of unit root in is in the logarithm format for all the variables (IIP, EXP, and CPI) at time t. The variable ?Yt-1 expresses the first differences with p lags and ?t is the
(5). (6).
variable that adjusts the error of auto-correlations. ?0 ,?,? and?i are the parameters to be estimated. The null and alternative hypothesis for the existence of unit root in variable Yt is However, ADF test losses power for sufficiently large values of p. Consequently, an additional, alternative test posited by Phillips and Peron (PP) (1987), which allows weak dependence and heterogeneity in residuals is conducted by following regression equation: H0: ?=0 H1: ?
Causality based export forecasting
1 CAUSALITY BASED EXPORT FORECASTING-FOR INDIAN ECONOMY Arti Omar2 This study is an attempt to identify future relationship of export with Index of Industrial production and Consumer price index by using Causality model. Export account for a significant proportion of Industrial value-added and employment due to the capability to innovate new products for commercial advantage and demonstrated capability to move export up the value-chain as wages rise. Future growth of export is totally depended on the future trade policies for import-export, fiscal policies, future foreign investment, and Industrial growth as well influenced by export prices in relation to world prices in other words inflationary and deflationary condition in India. *We thank to Dr. Pradip Swarnakar and Dr. Ajay kumar for their kind support during the study.
2
I.
INTRODUCTION
In India exports are growing everyday and you can access this vast market with various available data. India’s outstanding economic condition and its remarkable role in the global economy have lead to a great amount in further attention and research. India’s export has grown faster than the GDP over the past decades. GDP of any country helps in determining the growth rate and welfare of that country. GDP helps in determining the performance of any country. There have been many articles on the GDP increment of Indian economy; some of them said that the Gross Domestic Product (GDP) in India expanded 7.7 percent in the second quarter of 2011 over the previous quarter. The main factor that contributed to the Indian economy in 1990 was the opening of the Indian economy. From 2000 until 2011, India's average quarterly GDP Growth was 7.45 percent reaching an historical high of 11.80 percent in December of 2003 and a record low of 1.60 percent in December of 2002 [Trading Economics]. The export of any country is driven by factors like GDP and Industrial Production. Industrial Production is a very crucial factor in determining the growth of any country. Considering this fact we will be using GDP and Industrial Production as base and then accordingly determine the growth of the country. The factors which affect export are mainly the demand and the supply chain of any country. The demand chain of a country determines whether the export of that country is up to mark or not and the demand of any country is determined by the Industrial Production of that country. In a way all the three factors affect each other. After the foreign investment and free market openness factors, export has become the dominating factor to sustain economic growth of India. Export is one of the most fast growing sectors in India. As the export sector of Indian economy has always delineated impressive growth in all the areas of export. The engineering industry has been performing consistently over the years in the arena of exports as it secured the second position in terms of the earnings from exports in 2004-05, amounting to US $ 10516.45million, which increased to US $ 14587.37million in the next fiscal. The performance of textile industry has fluctuated a little as the earning of the textile industry from exports in the financial year 2004-05 was US $ 12204.71million which came down to US $ 12017.46 million in 2005-06. USA has turned out to be the most significant export partner of India and the export sector of Indian economy earned approximately US $ 13265.60 million in 2006-07. UAE has stood second only to USA as UAE contributed 9.7 out of the total Indian earnings from exports in 2006-07. UK and China has exchanged their positions in the current year as China's share among the exports figure in India in 2006-07 has improved by 6.3 % in comparison to 2005-06. In 2004-05 Belgium and Italy contributed substantially to the earnings from exports, with a contribution of US $ 2442.09 million [maps of India].
3 Here in our study we would be considering Industrial Production as our base to study the behavior of Indian export and use the data to forecast the future growth of Indian export theoretically. For computing the relation between the different variables we would be using co-integration model. Co-relation & Causality analysis of variables would tell us the relation between the various variables used in the study. The short run correlation between the variables helps to determine the relation and the direction of the relation among the variable, which would intern help in forecasting of export. The forecasting of the variable will help in predicting the future growth of that variable in India, and using this data India’s concerned authorities can take actions accordingly and this reduces risks of failures. So, objective of this study is to define long-run and short run relationship among stated variables (Export, Index of Industrial Production, and Consumer Price Index). This study is an attempt to find best fit solution of following hypothesis. 1. Does Export cause Industrial Production? 2. Does Industrial Production cause Export? 3. Does Export cause CPI? 4. Does CPI cause Export? 5. Does CPI cause Industrial Production? 6. Does Industrial Production cause CPI? 7. How export forecast is affected by Industrial production and CPI? In order to fulfill the research questions I have chosen quarterly data of Industrial Production, Export, and CPI in relatively same time frame. II. Literature Review
The world trade structure has been changing at a very rapid rate in the recent years. As the observation of the WTO report 2003 indicates following are the noteworthy features of the world trading system to emerge in the last few years. Firstly, the growth in the share of south-2 trade in the world trade owing largely to the growing liberalization of the trade and investment regimes of the developing countries. Secondly, it points to the decline and continued volatility of the commodity prices with factors such as trade policies in the developing countries, the structure of global international market for commodities and global macro-economic conditions, and lastly, the proliferation of regional trade agreements over the past decades or so, which have severe implication on the world trade structure, costs and standards (Rajesh Mehta, Parul Mathur [2003]).
4 India is moving at consistent rate towards establishing its presence in the world trade. In 2002, India ranked as the top 30th-leading exporter amongst the leading exporters in the world merchandise trade. Amongst the world’s leading importers in the world merchandise trade 2002, India ranked 24th (source: WTO). As indicated by India’s EXIM Mid Term Policy(2002-2007), India aims to have at least 1% share in the total global export from the level of the 0.71% in 2001-2002, implying a compound annual growth rate of 11.9% (in dollar terms) over the Tenth Five year Plan. 2002-2007. through the 1990’s while World trade value has increased 1.9 times, India’s export in U.S$ terms were up by 2.5 times. The percentage share of India’s exports in global exports remained more or less at around 0.52 percent during early 1009’s and showed a significant increase in 1995-96 to 0.60 percent remaining steady at around 0.63 thereafter till 1990. India’s export value sharply accelerated in 2000, from 35.66 U.S$ billion in 1991 to 43.3 U.S$ billion in 2001. But the year 2001 experienced the deepening and reinforcing of the global economic slowdown as depicted by the decline in the world trade form 6310.1 in 2001 to 6120.8 U.S$ billion in 2001. The prevailing global slowdown was accentuated further by the terrorist attack in the United States on December 11, 2001 which resulted in further downward growth projections for all most all major economic regions of the world. Increase in the value of India’s exports from 42.1 to 44.2 U.S$ billion. India’s share in global exports increased from 0.67 to 0.71 percent from 2000 to 2001. As the trends point out the year 2002 witnessed a substantial rise in India’s export with the growth rate of 18.6%. This led to a jump in the share of India’s exports from 0.71% to 0.80% in the global exports. From the phase of declining global exports and India’s export in the latter quarters of 2001, 2002 has been a phase of gradual but significant recovery (Rajesh Mehta, Parul Mathur [2003]). Goldstein and Khan (1985) studies was based on income and price effects in foreign trade. A beautiful combination of econometrics and trade modeling can be seen in this study, as well as this study employs the summary of various estimates of income elastic and related policy issues and price. Our first discussion will be based on recent studies have been done on export, that how export get affected by other macro-economic variables of a given country. According to Khan (1974) has investigated for the period 1951-1969 employing annual data for individual countries using the following model specification: (1). log MDit =a0 +a1 log
PMit PDit
This equation represents import demand function.
+a2 log Yit +Ut
5
domestic price level of country i, Yit is the real GNP of country i, Ut and is an error term associated with each observation. (2). log Xdit =b0 +b1 log(PX i /PWi )t =b2 log Wt + Vt
Where MDitis the quantity of imports of country i, PMit is the unit value of imports in country i, PDit is the
This equation represents export demand function
world price level, and W is the real world income. Since each variable is defined in logarithmic terms, the estimated coefficients are the elasticity of imports and exports with respect to the corresponding variables. These functions of import and export are estimated by using OLS (ordinary least square method). This study reveals that in developing countries price is the main factor that plays an important role. Secondly export as a demand function is described by Warner and Kreinin: (3). ln Xi =c+a1 ln YWi + a2 ln PXLCi + a3 lnEi + a4 ln EPi + a5 ln PFCcomp
Where Xdit is the quantity of exports of country i, PXi is the unit value of exports of country i, PWi is
rate, which is proxies by Ep =[0.7( log Et - log Et-1 )+0.3( log Et-1 - log Et-2 )]. PFCcomp is the average
effective exchange rate index of country I, (1975=100), EPi is the expected rate of change in the exchange export price of 64 competing countries expressed in foreign currencies, weighted by each competing country's exports into each of the markets. Having estimated the demand for imports and exports using OLS technique, the study of Warner and Kreinin employs that the introduction of floating exchange rates
where X i is the volume of the country's exports, YWi is the weighted average GDP of 23major importing countries facing country i, PXLCi is the export unit value index of the country i, 1974=100, Ei is the
appeared to have affected the volume of imports in several major countries, but the direction of change varied between them. In the competing countries exchange rate and the export price are found to be powerful determinants of a country's exports because the fact competing countries want to export to get foreign currency more in comparison to developed countries and developing countries. So exchange rate is an important factor whereas due to globalize competitive market competitive countries want to export the commodities under the range of economies of scale.
6 III. Data & Variables
This analysis selected three macro-economic variables such as Industrial Production (IP), Export (Exp), Consumer Price Index (CPI). GDP per capita National Income and Industrial production Index (IIP) are conversely used for economic growth. So, in this study we used the change in index of Industrial Production (IIP) for GDP growth rate. All three variables are converted into natural logarithm. All Analyses run with monthly data of all three variables over the period of 1994:4 through 2010:12. This monthly data set is obtained from OECD (Organization for Economic Co-operation and Development), Global finance Data, Reserve Bank of India, Asian Development Bank. IV. RESEARCH METHODOLOGY
With this study attempts are made to test and search for evidence of existence of relationship. This empirical analysis comprises of three parts. IV(i) UNIT ROOT TEST:
Under this section, study examines stationarity properties of data. For stationary series it is required series should have constant mean and variance through time and auto co-variance is not time varying for each given lag value (Enders, 2004). In stationary time series, shocks will be temporary and over the time their effects will be eliminated as the series revert to their long run mean values. Unlike stationary, nonstationary time series contains permanent components (Asteriou, 2007). Generally it is found that most of the economic variables show a trend and therefore most of the cases non-stationarity is found and these non-stationary time series can easily lead the Ordinary Least square (OLS) regression to incorrect or spurious conclusion. Thus, the key way to test for non-stationary is to test for existence for unit root or the order of integration (I) of variables. The present study employs the Augmented Dickey-Fuller (ADF, 1979, 1981), Phillips, and Perron (PP, 1988) test for unit root test. If all of the series are non-stationary in levels, it should be stationary in successively differences with the same level of lags. ADF test includes extra lagged terms of the dependent variables in order to eliminate auto-correlation. The lag length of this extra term is determined by the Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) Regression equation of ADF test takes following form: (4). ?Yt =?0 +?T+?Yt-1 + ?i=1 ?i ?Yt-1 +?t
p
The ADF regression test for existence of unit root in is in the logarithm format for all the variables (IIP, EXP, and CPI) at time t. The variable ?Yt-1 expresses the first differences with p lags and ?t is the
(5). (6).
variable that adjusts the error of auto-correlations. ?0 ,?,? and?i are the parameters to be estimated. The null and alternative hypothesis for the existence of unit root in variable Yt is However, ADF test losses power for sufficiently large values of p. Consequently, an additional, alternative test posited by Phillips and Peron (PP) (1987), which allows weak dependence and heterogeneity in residuals is conducted by following regression equation: H0: ?=0 H1: ?