Rank‐based Tests for Cross‐sectional Dependence in Large (N, T) Fixed Effects Panel Data Models

Abstract

AbstractMost existing methods for testing cross‐sectional dependence in fixed effects panel data models are actually conducting tests for cross‐sectional uncorrelation, which are not robust to departures of normality of the error distributions as well as nonlinear cross‐sectional dependence. To this end, we construct two rank‐based tests for (static and dynamic) fixed effects panel data models, based on two very popular rank correlations, that is, Kendall's tau and Bergsma–Dassios’ τ*, respectively, and derive their asymptotic distributions under the null hypothesis. Monte Carlo simulations demonstrate applicability of these rank‐based tests in large (N,T) case, and also the robustness to departures of normality of the error distributions and nonlinear cross‐sectional dependence.