Testing for Trend Specifications in Panel Data Models
提出一个非参数检验方法，用于检验面板数据模型中共同趋势的设定是否正确，允许异方差和误差项的相关性，并通过自助法改进小样本表现。
This paper proposes a consistent nonparametric test for common trend specifications in panel data models with fixed effects. The test is general enough to allow for heteroskedasticity, cross-sectional and serial dependence in the error components, has an asymptotically normal distribution under the null hypothesis of correct trend specification, and is consistent against various alternatives that deviate from the null. In addition, the test has an asymptotic unit power against two classes of local alternatives approaching the null at different rates. We also propose a wild bootstrap procedure to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test implemented with bootstrap <i>p</i>-values performs reasonably well in finite samples. Finally, an empirical application to the analysis of the US per capita personal income trend highlights the usefulness of our test in real datasets.