Causality between Indian Exports, Imports, and

Agricultural, Manufacturing GDP.

László Kónya and

Jai Pal Singh

Discussion Paper No. A07.02 ISBN 1 92137 7099 ISSN 1441 3213 July 2007

Causality between Indian Exports, Imports, and Agricultural, Manufacturing GDP∗

László Kónya1 and Jai Pal Singh2

1 Department of Economics and Finance La Trobe University, Melbourne, Australia

2 Department of Business Management

CCS Haryana Agricultural University, Hisar, India

July 2007

Abstract

Singh and Kónya (2006) studied the relationship between Indian GDP, exports, and imports from 1950/51 to 2003/2004. A logical further step is to investigate the same issue for two major sectors of the Indian economy: agriculture and manufacturing. In both sectors there is evidence of Granger causality between GDP and total exports, imports. In particular, agricultural GDP causes imports, exports cause agricultural GDP, and any two variables jointly cause the third one. There is also some evidence for agricultural GDP causing exports, and imports causing agricultural GDP, though these results are sensitive to model specification. As regards manufacturing, there seems to be two-way causality between manufacturing GDP and exports, imports cause manufacturing GDP, manufacturing GDP and imports jointly cause exports, and exports and imports jointly cause manufacturing GDP, but manufacturing GDP and exports do not seem to cause imports. Finally, there is also some evidence for manufacturing GDP causing imports, but this result is also sensitive to model specification.

JEL Classification Numbers: C22, F14, F40, O11 Key Words: causality, agricultural and manufacturing GDP, export, import, India.

∗ Corresponding author: Dr László Kónya, Department of Economics and Finance, La Trobe University. Address: Bundoora, VIC 3086, Australia. Telephone: +61 3 9479 2730. Fax: +61 3 9479 1654. Email: l.konya@latrobe.edu.au.

1. Introduction

This paper is the extension of a recent study by Singh and Kónya (2006) which analysed the relationship between Indian GDP, total exports, and total imports from 1950/51 to 2003/2004 using Wald tests within vector autoregressive (VAR) and vector error correction (VEC) models, and a modified Wald procedure in augmented level VAR systems. As regards the existence and/or direction of Granger-causality among these variables, some of the results were contradicting. Nevertheless, Singh and Kónya (2006) could safely conclude that exports and imports Granger-cause GDP both individually and jointly, GDP and exports jointly cause imports, and GDP and imports jointly cause exports, but GDP itself is unlikely to cause exports or imports. 1

The motivation behind the current paper is primarily due to Giles and Williams (2000, p. 335), who in the concluding remarks of their survey of the empirical literature on the export-led growth hypothesis pointed out that "The majority of papers surveyed focus on broad macroeconomic data and there are grounds for attention to less aggregated variables. For instance, Fosu (1990), Giles et al. (1992), Boltho (1996), Ghatak et al. (1997) and Tuan and Ng (1998) detect different conclusions for sector decompositions than at the broad macro level. We believe that much could be learned about the export-led growth question by assessing micro-based data." Earlier Eswaran and Kotwal (1993) argued that structural characteristics of particular exporting sectors may be an important consideration for growth, and, while commenting on Giles et al. (1992), Ahmad (2001) asserted that the relationship between exports and GDP growth may hold for particular sectors but not necessarily for an economy as a whole. Since in 2000 agriculture and manufacturing accounted for more than 40% of Indian GDP, it seems to be reasonable to study the relationship between GDP produced in these sectors and exports/imports. If, for example, agricultural GDP and total exports are cointegrated, then they share a long-run equilibrium relationship. This, in turn, has important macroeconomic policy implications, since any economic policy which affects total exports will also have an impact on the agricultural sector in the long run. Moreover, cointegration would allow the investigation of short-run dynamics, providing information on whether changes in agricultural GDP have impact on total exports in the short run, or vice versa. Although, ideally, this type of sectoral analyses should be based on disaggregated data, all the studies mentioned above compared sectoral exports to overall GDP. In this paper we do just the opposite, i.e. we compare agricultural and manufacturing GDP to total exports and imports. Our aim is to decide whether economic policy in India should promote international trade to speed up economic growth, or should it focus on the growth of the agricultural and manufacturing sectors which in turn will result in increased international trade. Specifically, the present paper addresses the following two questions: • Are agricultural/manufacturing GDP and total exports/imports cointegrated? • Does agricultural/manufacturing GDP Granger-causes total exports/imports or vice-versa? The emphasis in this paper is mainly on establishing the existence and direction of Granger causality between agricultural, manufacturing GDP and total exports, imports, rather than on

1 An earlier version of this paper, Kónya and Singh (2006), had been published in this Discussion Paper series.

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explaining the determinants of these relationships. Identification of causality can help policy makers to obtain a better insight of economic growth in India and to formulate effective economic policies and development strategies. Since traditionally India has a predominantly agricultural economy, especially in terms of employment, with huge potential for growth and export, this study has long-term trade economic policy implications for India. The overall conclusion of this study is that in both sectors there is a long-run equilibrium relationship and hence Granger causality between GDP and total exports, imports. In particular, agricultural GDP causes total imports, total exports cause agricultural GDP, and any two variables jointly cause the third one. Moreover, there is also some evidence for agricultural GDP causing total exports and total imports causing agricultural GDP, though these results are sensitive to model specification. As regards manufacturing, there is two-way causality between manufacturing GDP and total exports, total imports cause manufacturing GDP, manufacturing GDP and total imports jointly cause total exports, and total exports and total imports jointly cause manufacturing GDP, but manufacturing GDP and total exports do not seem to cause total imports. Finally, there is also some evidence for manufacturing GDP causing total imports, but this result is also sensitive to model specification. The rest of this paper is organised as follows. The relevant literature is reviewed in Section 2. The research methodology and the data set are discussed in Section 3. The empirical results are reported in Section 4. Finally, the summary conclusions and policy implications can be read in Section 5. 2. Literature Review

The export-led growth and growth-driven export hypotheses have become a bone of contention for researchers and policy makers alike for almost three decades. In spite of the large number of studies already published for and against these hypotheses, there is still no consensus, and new evidence based on new data sets and refined econometric methods is emerging every day. Some of the conflicting results are due to the different data sets. However, since there is not a single foolproof econometric technique for the detection of causality, some contradictions can be also explained by the different research methodologies.

Apart from Rashid (1995), we have not managed to find any study directly addressing causality between agricultural and/or manufacturing GDP and aggregate exports, imports of India. Therefore, as a second best option, we have decided to review papers which focus on the relationship between exports, imports (sectoral or aggregate) and GDP (sectoral or aggregate) of other countries.

Chow (1987), while studying exports and industrial development in eight export-oriented newly industrialised countries, detected causality from exports to manufacturing GDP growth in Mexico, bi-directional causality between these variables for Brazil, Hong Kong, Israel, Korea, Mexico, Taiwan, Singapore, but found no causality in the case of Argentina. Kovacic and Djukic (1990) found that in the former Yugoslavia real aggregate GDP, manufacturing GDP and real exports were not jointly cointegrated, but manufacturing GDP caused exports. Giles et al. (1992), who analysed the relationship in New Zealand between real GDP and exports of seven sectors, established that exports of live animals, and other food, beverages and tobacco cause GDP growth, and that economic growth led to increasing exports of metal

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products. However, in case of the total manufacturing sector they did not find evidence of causality in either direction, neither on the aggregate level. Suliman et al. (1994) found support for export-led growth in the manufacturing sector via changes in money supply for South Korea. Biswal and Dhawan (1998) showed that total exports, manufactured exports and real GDP of Taiwan are cointegrated, and that there is bi-directional causality among these variables. Khan and Saqib (1993) established positive relationships between real GDP, real exports, real manufacturing exports, and real primary exports in a study for Pakistan. Rashid (1995) found no significantly positive export/economic growth effect while studying growth in real GDP, exports, real investments, industrial production, imports, and agriculture for India. Arnade and Vasavada (1995), studying several Latin American and Asian countries, concluded that there was no causality between real agricultural output and agricultural exports in India. Shan and Sun (1998a), based on a multivariate analysis for Australia, found evidence for one-way causality running from manufacturing growth to export growth, but not for the export-led growth hypothesis in the aggregate level. In a similar study, Shan and Sun (1998b) reported bi-directional causality between exports and real industrial output for China. 3. Research Methodology and Data Research Methodology

This paper aims to establish the existence and direction of Granger causality, if any, between agricultural GDP and total exports, agricultural GDP and total imports, manufacturing GDP and total exports, as well as manufacturing GDP and total imports.

We use the concept of Granger causality, causality hereafter, in the sense defined by Granger (1969). Namely, a time series variable, say X, is said to be causal for another time series variable Y, if the history of X (xt-1, xt-2,..., x0 ) helps predict Y ( yt ) with greater accuracy. In other words, X is causing Y if X temporarily precedes Y, so changes in X take place before changes in Y. Similarly, variable Y is said to cause variable X if the former helps improve the forecasts of the latter.2

Suppose that (yt, xt), are jointly generated by the following bivariate vector autoregressive (VAR) process:

1 1, , 1, ,1 1

2 2, , 2, ,1 1

L L

t l i t l l i t ll l

L L

t l i t l l i t ll l

y y x

x y x

1,

2,

t

t

α β γ ε

α β γ

− −= =

− −= =

= + + +

= + + +

∑ ∑

∑ ∑ ε

(1)

where index t refers to the time period (t = 1, ..., T ) and l to the lag.

2 This notion of causality is based on the autoregressive representation of the yt, xt stochastic processes, while an alternative notion of causality due to Sims (1972) makes use of the moving average representation of these processes. In a way, Granger’s notion links causality to predictability and Sims’ notion is concerned with the shocks that affect the process. These two notions are equivalent in the bivariate case, but they are potentially different when more than two variables are involved. In this paper, we use causality in its Granger sense.

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As regards causality within this system, there are four possibilities. The first one, one-way or unidirectional causality from X to Y (denoted as x y→ ) occurs if in the first equation not all γ1,l’s are zero but in the second equation all β2,l’s are zero. Similarly, there is one-way causality from Y to X ( y x→ ) if in the first equation all γ1,l’s are zero but in the second not all β2,l’s are zero. Thirdly, there is two-way or bidirectional causality between Y and X ( x y ) if neither all γ1,l’s nor all β2,l’s are zero. Finally, there is no causality between Y and X ( /x y↔ ) if all γ1,l’s and β2,l’s are zero. In order to establish causality, we decided to use two complementary testing strategies, as advocated by Giles and Williams (2000), and very recently applied by Kónya (2004a, 2004b), and Singh and Kónya (2006). The first one, called the indirect approach, assumes that the variables are stationary or can be made so by differencing, and causality is tested with standard Wald tests within VAR (in levels and/or in first-differences) or vector error correction (VEC) models. The second strategy, referred to as the direct approach, requires less pretesting and is applied in an appropriately augmented level VAR model. Starting with the indirect approach, suppose that all variables in VAR system (1), including the ε1,i,t and ε2,i,t stochastic error terms, are stationary. In this case causality between X and Y can be tested with standard F/Wald-tests for zero restrictions, namely

1,1 1,2 1,... 0Lγ γ γ= = = = and . 2,1 2,2 2,... 0Lβ β β= = = =

However, if (1) contains non-stationary variables, the asymptotic distributions of these tests might be non-standard leading to spurious results and incorrect conclusions.3 Occasionally, the problem of non-stationarity can be addressed by applying some appropriate transformation to make the time series stationary. For example, if the variables are integrated of order one, denoted as I(1), then their first differences are stationary. Yet, if the variables are non-stationary but cointegrated, e.g. they are both I(1) and share a common stochastic trend, then a VAR in first differences (VARD) is inappropriate because it ignores the long run equilibrium relationship implied by the cointegration. In this case, causality should be studied using a vector error correction (VEC) model, which for the bivariate case can be written as

1 1

1 1 1 1, , 1, ,1 1

1 1

2 1 1 2, , 2, ,1 1

( )

( )

L L

t y t t l i t l l i t l tl l

L L

t x t t l i t l l i t ll l

y y x y x

x y x y x

1,

2,t

α α β δ φ ε

α α β δ φ

− −

− − − −= =

− −

− − − −= =

Δ = + − + Δ + Δ +

Δ = + − + Δ + Δ +

∑ ∑

∑ ∑ ε (2)

Within this framework, the EC term (yt-1 − β xt-1) provides an additional channel through which the variables can Granger-cause each other. Namely, non-causality from X to Y ( /x y→

/) implies αy = 0 and φ1,l = 0 for l = 1, …, L -1, while non-causality from Y to X

( y x→ ) implies αx = 0 and δ2,l = 0 for all l. It is also worth mentioning that if two I(1) variables are cointegrated, then there must be causality between them in at least one direction (Engle and Granger, 1987).

3 About this issue in particular and about testing for causality in general, see e.g. Enders (2004, pp. 281-287) and Lütkepohl and Krätzig (2004, pp.144-150).

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Before testing for causality with standard Wald tests, we have to study the properties of the time-series involved. If the time series are found stationary, the appropriate procedure is the Wald test in a level VAR (VARL) model. If both variables are I(1) but they are not cointegrated, causality can be tested with Wald tests in a VARD model. If the series are CI(1,1), we can perform Wald tests in a VEC model. In summary, the indirect approach involves three steps. Start with testing whether the data series are stationary. If the variables are deemed non-stationary (but integrated), test whether they are cointegrated. Finally, test for causality with Wald tests in a VARL, VARD, or VEC model, whichever is appropriate. Unlike the indirect approach, the direct approach to testing for causality does not require preliminary tests for unit root(s) and cointegration, and is applicable irrespective of the order of integration or cointegration present in the system. As already mentioned, in the presence of I(1) variables the Wald test statistic is likely to have non-standard asymptotic distribution. However, as Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996) pointed out, Wald tests that do not restrict the coefficients of all lagged terms under the null hypothesis still have their usual chi-square distribution. Consequently, given that all variables are I(0) or I(1), the direct approach to testing for Granger causality is performed by a so-called modified Wald (MWald) procedure, where a level VAR model augmented by an extra, redundant lag (VARAL) is estimated and a Wald test is performed on the first L non-redundant lags (i.e. t-1, t-2, …, t-L). In other words, the MWald test aims to remove singularity of the asymptotic distribution of the least squares estimators by fitting a VARAL model whose lag order exceeds the true order by the highest degree of integration in the system. Hence, we estimate a VARAL(L+d) model when the highest degree of integration is thought not to exceed d and the likely true lag order is L, irrespective of the presence of cointegration. The advantage of the direct approach is that it avoids the uncertainty often associated with unit-root and cointegration testing. However, the extra redundant lagged terms result in loss in efficiency and power, though the power loss is relatively small in trivariate or higher-order systems, for moderate to large sample sizes, and for systems in which the true lag order is large. The studies undertaken by Giles and Mirza (1999) and Clarke and Mirza (2003) show that the MWALD test performs consistently well over a wide range of systems, including near-integrated, stationary and mixed integrated and stationary systems; cases for which the indirect approach tends to over detect causality. Since we decided to apply both strategies, it is important to establish the time-series properties of the data. Due to the generally low power of unit root tests, we have performed five different tests, namely, the augmented Dickey-Fuller (ADF) test, the Dickey-Fuller test with GLS detrending (DF-GLS), the Elliot-Rothenberg-Stock (ERS) point optimal test, the Dickey-Pantula (DP) test for at most two unit roots, and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for stationarity.4 Similarly to unit-root / stationarity tests, there are many alternative cointegration tests. To this end, we have applied two maximum likelihood tests, Johanson’s trace (JT) and maximum eigenvalue (JME) tests. The first tests the null hypothesis of at most r cointegrating vectors against the alternative hypothesis of more than r

4 About the unit-root and cointegration tests used in this study see e.g. Maddala and Kim (1998), Part II.

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cointegrating vectors, while the second tests the null hypothesis of r cointegrating vectors against the alternative hypothesis of r + 1 cointegrating vectors. Data

We used annual data for fifty-four years from 1950/51 to 2003/04 on Indian exports, imports and agricultural, manufacturing GDP at current prices in Indian Rupees. These data were primarily downloaded from the database of the Reserve Bank of India. As there are discrepancies among various sources, the data were cross-checked with some other publications and websites, such as the Directorate General of Commercial Intelligence and Statistics (Ministry of Commerce), National Accounts Statistics (Central Statistical Organisation, Ministry of Statistics and Programme Implementation, Government of India), Planning Commission of India and various issues of Economic Surveys.

All data series were transformed to natural logarithms before further analysis, so that the first differences can be interpreted as growth rates. The data series are denoted as LNAGDP (log of agricultural GDP), LNMGDP (log of manufacturing GDP), LNEXP (log of total exports), and LNINMP (log of total imports), respectively.5 They are displayed in Figure 1. 4. Empirical Results Data Properties Unit root/stationarity test results

Unit-root / stationarity tests have been performed on the levels and first differences of the series. Aggregate exports and imports had already been tested for unit/stationarity in Singh and Kónya (2006) and they were deemed to be I(1). As regards the agricultural and manufacturing GDP series, Figure 1 reveals that they are steadily increasing. Therefore, their levels were tested assuming that the data generating processes have a linear trend component, while in case of the first differences only a drift term was used. 5 All data manipulations and further calculations were performed with EViews 5.1.

7

5

6

7

8

9

10

11

12

13

14

15

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

LNAGDP LNMGDP LNEXP LNIMP

Figure 1: The logarithms of Indian agricultural and manufacturing GDP, aggregate exports and imports

The results are summarized in Table 1.6 The DF-GLS, ERS and DP tests suggest that both LNAGDP and LNMGDP are I(1). The ADF also implies that LNAGDP is I(1), but it rejects this option for LNMGDP at the 10% level. Finally, KPSS firmly rejects stationarity for the level series, and at the 10% level it also rejects this hypothesis for the first differences. In spite of these weak rejections, we conclude that all four data series are I(1). Cointegration test results

As all four series seem to be I(1), first-difference stationary, the next step is to test for cointegration in bivariate and trivariate systems. The results are shown in the Tables 2 and 3.

For agriculture, within the bivariate systems both tests reject the null hypothesis of no cointegration between GDP and exports, and between GDP and imports, respectively, though in the latter case the results are lag sensitive. In the trivariate system cointegration is supported among the variables by both tests, though the results are again lag sensitive. Namely, allowing for no lag there seems to be a single cointegrating relation, while for 9 lags both tests detect two independent cointegrating vectors.

6 In each table *, **, and *** indicate significance at the 10, 5, and 1% levels.

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Table 1: Unit Root and Stationarity Test Results for the GDP in Agriculture and Manufacturing

Variables

LNAGDP LNMGDP ΔLNAGDP ΔLNMGDP Unit-Root / Stationarity Tests

Lag Test stat. Lag Test stat. Lag Test stat. Lag Test stat.ADF 0

-3.093 1 -3.409* 0

2 -7.141*** -3.180**

0 -5.927***

DF-GLS 0

-1.541 0 1

-1.379 -1.507

0 2

-6.644*** -2.688***

0 -5.958***

ERS 0

48.410 1 62.183 0 2

1.181*** 2.812**

0 0.947***

NP 0

-1.159 0 1

-0.977 -1.228

0 2

-3.900*** -1.733*

0 -4.120***

DP step 1 0 -7.141*** 3 -5.328*** step 2 0 1.308 3 0.624

KPSS 5 0.218*** 5 0.227*** 4 0.396* 3 0.428*

Notes: a) ADF: Augmented Dickey-Fuller test; DF-GLS: Dickey-Fuller test with GLS detrending; ERS: Elliot-Rothenberg-Stock Point Optimal test; KPSS: Kwiatkowski-Phillips-Schmidt-Shin test; DP: Dickey-Pantula test for at most two unit roots.

b) Allowing for a maximum lag length of 10 years, the lag lengths are selected by minimizing the Schwarz and Akaike Information Criteria. In KPSS, lag denotes the bandwidth selected on the basis of the Newey-West method using Bartlett kernel.

c) The test equations for the level series have a deterministic linear trend, while for the first difference series they have only a constant term.

For manufacturing, within the bivariate systems both tests detect cointegration between GDP and exports, and between GDP and imports, respectively. Within the trivariate framework with no lag there seems to be a single cointegrating vector, while for 9 lags JME detects a single cointegrating vector but JT finds three. However, since this latter scenario implies that LNAGDP, LNEXP and LNIMP are all stationary, in the light of the unit root / stationarity and bivariate cointegration test results, we reject it as implausible. To sum up the cointegration results, in spite of some sensitivity to the lag structure, we can safely conclude that there is a long-run equilibrium relationship between agricultural, manufacturing GDP and total exports, imports, both within the bivariate and trivariate frameworks.

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Table 2: Cointegration Test Results for Agricultural GDP, Total Exports and Imports

Variables

Cointegration Tests

LNAGDP LNEXP

LNAGDP LNIMP

LNAGDP LNEXP LNIMP

Lag H0 Test stat. Lag H0 Test stat. Lag H0 Test stat. JT 0 r = 0

r ≤ 1

24.263*** 0.586

0 r = 0 r ≤ 1

11.114 1.598

0 r = 0 r ≤ 1 r ≤ 2

37.313*

12.442 1.049

1 r = 0 r ≤ 1

22.159*** 0.007

8 r = 0 r ≤ 1

17.861** 0.036

9 r = 0 r ≤ 1 r ≤ 2

82.540*** 30.700*** 1.332

JME 0 r = 0 r ≤ 1

23.677*** 0.586

0 r = 0 r ≤ 1

9.516 1.598

0 r = 0 r ≤ 1 r ≤ 2

24.871*** 11.373 1.049

1 r = 0 r ≤ 1

22.151*** 0.007

8 r = 0 r ≤ 1

17.825** 0.036

9 r = 0 r ≤ 1 r ≤ 2

51.840*** 29.368*** 1.332

Notes: a) JT: Johansen’s Trace test; JME: Johansen’s Maximum Eigenvalue test. b) Lag refers to lag of the first differences. Allowing for a maximum lag length of 10 years,

the lag lengths are selected by minimizing the Schwarz and Akaike Information Criteria. c) The level data are assumed to have linear trends but the cointegrating equations

have only intercepts (Case 3 in EViews 5).

Table 3: Cointegration Test Results for Manufacturing GDP, Total Exports and Imports

Cointegration Tests Variables

LNMGDP LNEXP

LNMGDP LNIMP

LNMGDP LNEXP LNIMP

Lag H0 Test stat. Lag H0 Test stat. Lag H0 Test stat. JT 0 r = 0

r ≤ 1

22.684*** 0.273

0 r = 0 r ≤ 1

15.495**

0.157 0 r = 0

r ≤ 1 r ≤ 2

37.886*** 10.775 0.735

1 r = 0 r ≤ 1

23.356*** 0.440

1 r = 0 r ≤ 1

15.950** 0.158

9 r = 0 r ≤ 1 r ≤ 2

55.428*** 16.876** 5.294**

JME 0 r = 0 r ≤ 1

22.411*** 0.273

0 r = 0 r ≤ 1

15.734**

0.157 0 r = 0

r ≤ 1 r ≤ 2

27.112*** 10.039 0.735

1 r = 0 r ≤ 1

22.917*** 0.440

1 r = 0 r ≤ 1

15.792** 0.158

9 r = 0 r ≤ 1 r ≤ 2

38.552*** 11.582 5.294**

Notes: see Table 2. Granger Causality Test Results

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Wald Granger Causality Tests - Indirect Approach

Since considering only two variables at a time we established cointegration between sectoral GDP and total exports and also between sectoral GDP and total imports for both agriculture and manufacturing, first we performed Wald tests using VECM of two endogenous variables. The results are shown in Tables 4 and 5.

As regards agriculture, LNAGDP clearly causes both LNEXP and LNIMP, and LNEXP also causes LNAGDP. Hence, there is two-way causality between agricultural GDP and total exports, but only one-way causality between agricultural GDP and total imports, running from the former to the latter. In the case of manufacturing, any of the three variables causes the other two individually, at least via the error correction term. Therefore, there is two-way causality between manufacturing GDP and total exports, and also between manufacturing GDP and total imports.

Table 4: WALD Granger Causality Tests for Agricultural GDP, Total Exports and Imports in VECM of Two Endogenous Variables

Models

VECM Null hypothesis Rank

Lag t-stat χ2-stat LNAGDP ⏐ LNEXP 1 0

10 5.345*** 2.568***

16.206*

LNEXP ⏐ LNAGDP 1 0 10

1.262 -0.392

21.249**

LNAGDP ⏐ LNIMP 1 0 10

3.164*** 1.683**

23.956***

LNIMP ⏐ LNAGDP 1 0 10

0.793 -0.665

9.276

Notes: a) Rank refers to the cointegrating rank and lag to lags of the first differences. The optimal lag length is selected by minimizing the Schwarz and Akaike Information Criteria in level VAR, allowing for a maximum lag length of 11 years. If the residuals generated by the optimal lag length are autocorrelated, heteroscedastic or non-normal, the lag length is gradually increased.

b) The models were subjected to three residual tests: Breusch-Godfrey LM test for general, higher order (up to order 10) ARMA errors, White test for heteroscedasticity (without cross terms) and Jarque-Bera test for normality. At the 5% significance level the VECM(10) models have non-normal errors.

c) In VECM Granger causality is tested by the t-statistic (which is asymptotically normal) on the speed of adjustment coefficient (α) and by a Wald (χ2) test on the coefficients of the lagged first-differences of the hypothesized causal variable.

d) The level data are assumed to have linear trends but the cointegrating equations have only intercepts (i.e. Case 3 in EViews 5).

e) The symbol ⏐ means "does not Granger cause".

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Table 5: WALD Granger Causality Tests for Manufacturing GDP, Total Exports and Imports in VECM of Two Endogenous Variables

Models

VECM Null hypothesis Rank

Lag t-stat χ2-stat LNMGD ⏐ LNEXP 1 0

10 5.045*** 3.160***

26.614***

LNEXP ⏐ LNMGDP 1 0 10

2.733*** 1.632*

15.628

LNMGD ⏐ LNIMP 1 0 10

3.501*** 2.043**

3.564

LNIMP ⏐ LNMGDP 1 0 10

3.444*** 2.113**

12.163

Notes: See Table 4. At the 5% significance level VECM(0) for LNMGDP and LNIMP suffers from heteroscedasticity and the VECM(10) models have non-normal errors.

Table 6: WALD Granger Causality Tests for Agricultural GDP, Total Exports and Imports in VECM of Three Endogenous Variables

Model

VECM Null hypothesis Rank

Lag t-stat χ2-stat LNAGDP ⏐ LNEXP 1

2 10 10

3.677 13.394

LNEXP ⏐ LNAGDP 1 2

10 10

194.076*** 211.657***

LNIMP ⏐ LNAGDP 1 2

10 10

128.043*** 169.686***

LNAGDP ⏐ LNIMP 1 2

10 10

38.757*** 43.708***

LNAGDP, LNIMP ⏐ LNEXP 1

2

0 10 10

5.468***

-0.162 2.306** -2.416***

16.359 29.386*

LNAGDP, LNEXP ⏐ LNIMP 1

2

0 10 10

2.021** 1.303 -0.424 2.080*

70.354*** 78.258***

LNEXP, LNIMP ⏐ LNAGDP 1

2

0 10 10

1.157 -6.151*** -5.978*** -3.063***

308.464*** 384.311***

Notes: See Table 4. At the 5% significance level the VECM(10) models have non-normal errors.

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Tables 6 and 7 display the results based on VECM of three endogenous variables. We have considered two possibilities: one variable is causing another, or two variables are causing the third. In case of agriculture, LNAGDP causes LNIMP but not LNEXP, while both LNEXP and LNIMP cause LNAGDP. In other words, unlike in the bivariate framework, there seems to be one-way causality running from total exports to agricultural GDP and two-way causality between agricultural GDP and total imports. In case of manufacturing, LNMGDP causes LNEXP but not LNIMP, while both LNEXP and LNIMP cause LNMGDP. Not surprisingly, the tests on two causal variables at a time suggest that most likely in both sectors any two variables together cause the third, with the possible exception of manufacturing GDP and total exports jointly causing total imports.

Table 7: WALD Granger Causality Tests for Manufacturing GDP, Total Exports and Imports in VECM of Three Endogenous Variables

Model

VECM Null hypothesis Rank

Lag t-stat χ2-stat LNMGDP ⏐ LNEXP 1 10 29.118***

LNEXP ⏐ LNMGDP 1 10 27.004*** LNIMP ⏐ LNMGDP 1 10 25.917*** LNMGDP ⏐ LNIMP 1 10 2.838 LNMGDP, LNIMP ⏐ LNEXP 1 0

10 5.367*** 2.731***

69.143***

LNMGDP, LNEXP ⏐ LNIMP 1 0 10

3.489*** 0.685

7.893

LNEXP, LNIMP ⏐ LNMGDP 1 0 10

3.423*** -0.253

46.334***

Notes: See Table 4. At the 5% significance level the VECM(10) model has non-normal errors.

Modified Wald Granger Causality Tests Results - Direct Approach The result based on modified Wald tests in VARAL models of three endogenous variables can be seen in Tables 8 and 9. Clearly, this time the MWALD tests confirm the previous conclusions. Namely, for agriculture, the MWALD tests indicate that LNEXP causes LNAGDP and that there is two-way causality between LNAGDP and LNIMP. There are also indications that LNAGDP and LNIMP cause LNEXP, LNAGDP and LNEXP cause LNIMP, and LNEXP and LNIMP cause LNAGDP, though some of these results are lag sensitive. For manufacturing, the MWALD tests detect two-way causality between LNMGDP and LNEXP, and one-way causality from LNIMP to LNMGDP. Finally, LNMGDP and LNIMP jointly cause LNEXP, and LNEXP and LNIMP jointly cause LNMGDP.

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Table 8: MWALD Granger Causality Tests for Agricultural, Total Exports and Imports in VARAL Models of Three Endogenous Variables

Model

VARAL(p+d) Null hypothesis

Lag Order χ2-stat 1 1 0.768 LNAGDP ⏐ LNEXP

10 1 11.674 1 1 3.972* LNEXP ⏐ LNAGDP

10 1 265.770***

1 1 0.046 LNAGDP ⏐ LNIMP 10 1 54.300***

1 1 0.040 LNIMP ⏐ LNAGDP 10 1 140.374***

1 1 0.855 LNAGDP, LNIMP ⏐ LNEXP 10 1 35.717**

1 1 2.283 LNAGDP, LNEXP ⏐ LNIMP 10 1 103.733***

1 1 5.791* LNEXP, LNIMP ⏐ LNAGDP 10 1 542.357***

Notes: See Table 4. a) Lag is the optimal lag order of a level VAR chosen by minimizing the

Schwarz and Akaike Information Criteria (p) and order is the highest degree of integration in the system (d).

b) At the 5% significance level VARAL(11) has non-normal errors. c) Granger causality is tested by Wald (χ2) test on the coefficients of the first

p lags of the hypothesized causal variable. d) The level data are assumed to have linear trends.

5. Summary and Policy Implications

This article extends the earlier work of Singh and Kónya (2006) where the focus was on

the aggregate level to ascertain cointegration and possible Granger causality between Indian GDP, exports, and imports bivariate and trivariate frameworks. The results of Singh and Kónya (2006) indicated that exports and imports, both individually and jointly, cause GDP, supporting the export-, import-led growth hypotheses. However, there was hardly any support for the growth-driven export and import hypotheses. Since agriculture and manufacturing are two major sectors of the Indian economy, this paper goes one step further by investigating the relationship between (the logarithms of) GDP produced in these sectors and (the logarithms of) total exports, imports. In order to establish causality, two complementary testing strategies have been used. The first, called indirect approach, assumes that the variables are stationary or can be made so by differencing, and causality is tested with standard Wald tests within VAR (in levels and/or in first-differences) or VEC models. The second, referred to as direct approach, requires less pretesting and is applied in an appropriately augmented level VAR model.

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Table 9: MWALD Granger Causality Tests for Manufacturing GDP, Total Exports and Imports in VARAL Models of Three Endogenous Variables

Model

VARAL(p+d) Null hypothesis

Lag Order χ2-stat 1 1 0.018 LNMGDP ⏐ LNEXP

10 1 21.127**

1 1 0.424 LNEXP ⏐ LNMGDP 10 1 62.006***

1 1 1.823 LNMGDP ⏐ LNIMP 10 1 9.188 1 1 0.512 LNIMP ⏐ LNMGDP

10 1 48.889***

1 1 0.043 LNMGDP, LNIMP ⏐ LNEXP 10 1 63.197***

1 1 2.431 LNMGDP, LNEXP ⏐ LNIMP 10 1 23.018 1 1 0.698 LNEXP, LNIMP ⏐ LNMGDP

10 1 99.447***

Notes: See Table 8. At the 5% significance level VARAL(11) has non-normal errors.

The preliminary unit root/ stationarity tests suggested that agricultural, manufacturing GDP, total exports, and total imports are all I(1). In spite of some sensitivity to the lag structure, from the subsequent cointegration analysis we concluded for both agriculture and manufacturing that there is a long-run equilibrium relationship between sectoral GDP and total exports, imports, within bivariate and trivariate frameworks alike. On the basis of the indirect approach we concluded that there is two-way causality between manufacturing GDP and total exports, total exports cause agricultural GDP, agricultural GDP causes total imports, and that total imports cause manufacturing GDP. Though with less certainty, we could also claim that agricultural GDP causes total exports, total imports cause agricultural GDP, and manufacturing GDP causes total imports. Finally, in both sectors any two variables together cause the third, except that manufacturing GDP and total exports do not seem to cause total imports. As regards the direct approach, we have applied it only within the trivariate framework, where by and large the conclusions are in agreement with that of the indirect approach. Agriculture and manufacturing together drive most business activities in India. In the absence of direct studies relating agricultural, manufacturing GDP and total exports, imports, the economic policy implications of our findings on Indian agriculture, manufacturing, and international trade can be argued only indirectly. McKinnon (1964), for example, states that in most developing countries lack of technology is the main obstacle to economic growth and the ‘foreign exchange gaps’ impedes developing countries from financing the imports of these technologies. Exports of labour-intensive goods could fill the ‘foreign exchange gaps’ and could finance the import of appropriate technology into domestic economy. Rana and Dowling (1990) argue that by enabling the imports of essential raw materials and capital goods, exports indirectly contribute to the investment in the economy and thereby to higher

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