Short- and long-run rolling causality techniques and optimal window-wise lag selection: an application to the export-led growth hypothesis

Abstract

The literature devoted to the export-led growth (ELG) hypothesis, which is of utmost importance for policymaking in emerging countries, provides mixed evidence for the validity of the hypothesis. Recent contributions focus on the time-dependence of the relationship between export and output growth using rolling causality techniques based on vector autoregressive models. These models focus on a short-term view which captures single policy-induced developments. However, long-term structural changes cannot be covered by examinations related to the short-term. This paper hence examines the time-varying validity of the ELG hypothesis for India for the period 1960-2011 using rolling causality techniques for both the short-run and long-run horizon. For the first time, window-wise optimal lag-selection procedures are applied in connection with these techniques. We find that exports long-run caused output growth from 1997 until 2009 which can be seen as a consequence of political reforms of the 1990s that boosted economic growth by generating foreign direct investment opportunities and higher exports. For the short-run, export significantly caused output in the period 1998-2003 which followed a concentration of liberalization measures in 1997. Causality in the reversed direction, from output to exports, only seems to be relevant in the short-run.

Structural Break and Causal Analyses of U.S. Corn Use for Ethanol and Other Corn Market Variables

“…Although multivariate analysis introduces potential non-linearity into the causal relationship between two given variables, an analysis of direct causality between two variables in the multivariate framework produces valid one-step ahead linear causality results. Implementation of the causality analysis in this study is similar to other studies [34][35][36]. The augmented-VAR approach of Toda and Yamamoto [37], henceforth referred to as TY in this paper, is used for the Granger causality (GC) analysis.…”

“…The TY procedure may reject the null hypothesis too often (Type I error) [34], and small samples can lead to low power (Type II error) of the causality tests. Wild bootstrap simulations are used in this paper to address potential Type I and II errors.…”