Rank‐based Tests for Cross‐sectional Dependence in Large (N, T) Fixed Effects Panel Data Models
针对固定效应面板数据模型，提出了两种基于秩相关的截面依赖性检验方法，分别使用Kendall's tau和Bergsma-Dassios τ*，在大样本下渐近分布已知，且对误差非正态和非线性依赖具有稳健性。
Abstract 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’ 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.