Feasible bias-corrected OLS, within-groups, and first-differences estimators for typical micro and macro AR(1) panel data models
Feasible bias-corrected OLS, within-groups, and first-differences estimators for typical micro and macro AR(1) panel data models
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适用于典型微观和宏观 AR(1) 面板数据模型的可行偏差校正 OLS、组内和一阶差分估计量
DOI：
10.1007/s00181-005-0256-6
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发表时间：
2005
影响因子：
3.2
通讯作者：
Joaquim J. S. Ramalho
中科院分区：
文献类型：
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作者：
Joaquim J. S. Ramalho
在本文中，我们提出了几种替代的方法来构建可行的偏差校正（FBC）合并最小二乘，组内，和第一差异估计AR（1）面板数据模型。在一项涉及微观经济学家和宏观经济学家通常遇到的质量数据的蒙特卡罗模拟研究中，我们发现，所提出的估计量似乎比通常在这种情况下使用的GMM估计量具有更好的有限样本性质：大多数FBC估计是无偏的，即使当时间序列是高度持久的，显示较少的变化，并且不受个体效应和特异质误差方差的相对大小的影响。
In this paper we suggest several alternative ways of constructing feasible bias-corrected (FBC) pooled least squares, within-groups, and first-differences estimators for AR(1) panel data models. In a Monte Carlo simulation study involving data with the qualities normally encountered by both microeconomists and macroeconomists we found that the estimators proposed seem to possess better finite sample properties than the GMM estimators usually employed in this setting: most FBC estimators are unbiased, even when the time series is highly persistent, display less variability, and are not affected by the relative magnitude of the variances for the individual effect and the idiosyncratic error.