Test for serial correlation in panel data models with interactive fixed effects

This article considers a consistent test for serial correlation of unknown form in the residual of panel data models with interactive fixed effects and possibly lagged dependent variables. Following the spirit of Hong, we construct a test statistic based on the comparison of a kernel-based spectral density estimator and the null spectral density. Under the null hypothesis, our test statistic is asymptotically <i>N</i>(0, 1) as both <i>N</i> and <i>T</i> tend to infinity. In contrast to existing tests for serial correlation, there is no need to specify the order of serial correlation about the alternative. We further examine the local and global power properties of test. A simulation study shows that our test performs well in finite samples. In the empirical application, we apply the test to study the impact of the divorce law reform on divorce rate. We find strong evidence of serial correlation in the residual, and our results show that the divorce law reform has permanent positive effects on divorce rates.

本文针对带有交互固定效应（interactive fixed effects）且可能包含滞后因变量（lagged dependent variables）的面板数据模型的残差序列，构建了一种用于检验未知形式序列相关的一致检验方法。秉承Hong的研究思路，本文基于核谱密度估计量（kernel-based spectral density estimator）与原假设谱密度（null spectral density）的比较构造检验统计量。在原假设下，当截面个体数N与时期数T均趋于无穷时，该检验统计量渐近服从标准正态分布N(0,1)。与现有序列相关检验方法不同，本文无需针对备择假设指定序列相关的阶数。本文进一步考察了该检验的局部与全局检验功效特性。蒙特卡洛模拟实验表明，本文提出的检验在有限样本下表现优异。在实证应用中，我们将该检验用于考察离婚法改革对离婚率的影响，发现残差存在显著的序列相关，且结果显示离婚法改革对离婚率具有持久的正向影响。