A Homogeneous Approach to Testing for Granger Non-Causality in Heterogeneous Panels

Arturas Juodis (), Yiannis Karavias and Vasilis Sarafidis

No 32/20, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies on the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross-sections guarantees that the estimator has a root NT convergence rate. In order to account for the well-known "Nickell bias", the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks' profitability and cost efficiency.

Keywords: panel data; Granger causality; VAR; "Nickell bias"; bias correction; fixed effects (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 C33 (search for similar items in EconPapers)
Pages: 21
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Journal Article: A homogeneous approach to testing for Granger non-causality in heterogeneous panels (2021)
Working Paper: A Homogeneous Approach to Testing for Granger Non-Causality in Heterogeneous Panels (2020)
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