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

Juodis, Arturas and Karavias, Yiannis and Sarafidis, Vasilis (2020): A Homogeneous Approach to Testing for Granger Non-Causality in Heterogeneous Panels.

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 sqrt(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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Panel Data; Granger Causality; VAR; Nickell bias; Bias Correction; Fixed Effects
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models

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