Testing for shifts in a time trend panel data model with serially correlated error component disturbances
研究了在面板数据时间趋势模型中检验移位的方法，无需事先知道误差项是否平稳，提出了适用于平稳和非平稳误差的Wald检验，并通过蒙特卡洛模拟考察了有限样本性质。
This paper studies testing of shifts in a time trend panel data model with serially correlated error component disturbances, without any prior knowledge of whether the error term is stationary or nonstationary. This is done in case the shift is known as well as unknown. Following the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator. The proposed test has a chi-square limiting distribution and is valid for both I(0) and I(1) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations