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

Most 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'tau*, 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.