A Heteroskedasticity-RobustF-Test Statistic for Individual Effects
推导了静态线性面板数据模型中固定效应F检验统计量在误差项非正态和异方差下的渐近分布，提出一种简单线性变换得到异方差稳健的F检验，并证明其比稳健随机效应检验有更高的渐近功效。蒙特卡洛模拟显示，自助法版本在有限样本中推断更可靠。
We derive the asymptotic distribution of the standard F-test statistic for fixed effects, in static linear panel data models, under both non-normality and heteroskedasticity of the error terms, when the cross-section dimension is large but the time series dimension is fixed. It is shown that a simple linear transformation of the F-test statistic yields asymptotically valid inferences and under local fixed (or correlated) individual effects, this heteroskedasticity-robust F-test enjoys higher asymptotic power than a suitably robustified Random Effects test. Wild bootstrap versions of these tests are considered which, in a Monte Carlo study, provide more reliable inference in finite samples.