A computationally simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods is fixed or tends to infinity with the number of panel units. The approach can accommodate both fixed-effects and random-effects assumptions, heteroskedastic errors, as well as higher-order autoregressive models. Panel-corrected standard errors are proposed that allow for robust inference in dynamic models with cross-sectionally correlated errors. Monte Carlo experiments suggest that under the assumption of strictly exogenous regressors the bias-corrected method of moment estimator outperforms popular GMM estimators in terms of efficiency and correctly sized tests.

提出了一种计算简单的线性动态面板数据模型的偏差校正，并研究了其在时间段数固定或随着面板单元数趋于无穷大时的渐近特性。该方法可以适应固定效应和随机效应假设、异方差误差以及高阶自回归模型。提出了面板校正的标准误差，允许在具有横截面相关误差的动态模型中进行稳健的推理。蒙特卡罗实验表明，在严格外生回归量的假设下，矩估计量的偏差校正方法在效率和正确大小的测试方面优于流行的 GMM 估计量。