Unit Root Inference in Generally Trending and Cross-Correlated Fixed-T Panels
提出一种基于广义矩估计的新面板单位根检验方法，适用于时间期数T小、截面单元N大的面板，允许趋势函数无限制且误差有多因子结构，蒙特卡洛模拟显示小样本性质良好，并用美国银行数据检验吉布拉定律。
This article proposes a new panel unit root test based on the generalized method of moments approach for panels with a possibly small number of time periods, T, and a large number of cross-sectional units, N. In the model that we consider the deterministic trend function is essentially unrestricted and the errors obey a multifactor structure that allows for rich forms of unobserved heterogeneity. In spite of these allowances, the GMM estimator considered is shown to be asymptotically unbiased, -consistent, and asymptotically normal for all values of the autoregressive (AR) coefficient, ρ, including unity, making it a natural candidate for unit root inference. Results from our Monte Carlo study suggest that the asymptotic properties are borne out well in small samples. The implementation is illustrated by using a large sample of US banking institutions to test Gibrat’s Law.