Bias-corrected method of moments estimators for dynamic panel data models
Bias-corrected method of moments estimators for dynamic panel data models
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DOI：
10.1016/j.ecosta.2021.07.001
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发表时间：
2022-09-24
影响因子：
1.9
通讯作者：
Hayakawa, Kazuhiko
中科院分区：
文献类型：
--
作者：
Breitung, Joerg;Kripfganz, Sebastian;Hayakawa, Kazuhiko
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方法，并研究了当周期数随面板数量的增加而固定或趋于无穷大时的偏差校正方法的渐近性质。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。提出了面板校正标准误差，允许在具有横截面相关误差的动态模型中进行鲁棒推断。蒙特卡罗实验表明，在严格外生回归的假设下，矩估计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。(C) 2021经济计量与统计。Elsevier B.V.版权所有。
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. (C) 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.