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- W2187718562 abstract "The long-run impact of export growth on the growth of the entire economy is a key issue for policymakers in emerging countries. There is a vast body of empirical research with respect to this issue, especially addressing the case of India, in which cointegration techniques as well as long-run Granger causality analysis are applied to exports and GDP series. However, results are inconclusive which can be attributed to differences in the choice of econometric approaches, the considered sample period and omitted variable biases. To address these issues, we apply both linear as well as non-parametric cointegration techniques for non-linear functional forms, we apply an innovative bootstrapping procedure to examine the robustness of results with respect to the selection of sample periods, and we include imports as third variable as imports may be the relevant variable with a causal link towards GDP instead of exports. We show that there is linear cointegration between exports and GDP which is likely to render the nonparametric cointegration test ineffective. Moreover, we obtain the result that the selection of the sample period is an important determinant for the inference whether there is cointegration or not. We find that evidence for the causality from exports to GDP is less robust than the causality from GDP to exports." @default.
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- W2187718562 date "2014-01-01" @default.
- W2187718562 modified "2023-09-26" @default.
- W2187718562 title "COINTEGRATION AND CAUSALITY BETWEEN INDIA'S EXPORTS AND GDP: A SAMPLE PERIOD-ROBUST TESTING FRAMEWORK" @default.
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