论文标题
前向正交偏差GMM和没有大样本偏见
Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias
论文作者
论文摘要
众所周知，当时间段（$ t $）与横截面单元的数量（$ n $）相比，动态面板数据回归的广义方法（GMM）估计器可能会有很大的偏差。偏见归因于使用许多仪器变量。本文表明，如果以$ t $比$ t^{1/2} $慢的$ t $中使用的最大仪器变量数量增加，那么利用远期正交偏差（FOD）变换的GMM估计器没有划定的差异，无论其相对$ t $的快速增加了$ t $的差异。该结论特定于使用FOD变换。当使用其他转换来消除固定效果时，不一定适用类似的结论。提供了说明分析结果的蒙特卡洛证据。
It is well known that generalized method of moments (GMM) estimators of dynamic panel data regressions can have significant bias when the number of time periods ($T$) is not small compared to the number of cross-sectional units ($n$). The bias is attributed to the use of many instrumental variables. This paper shows that if the maximum number of instrumental variables used in a period increases with $T$ at a rate slower than $T^{1/2}$, then GMM estimators that exploit the forward orthogonal deviations (FOD) transformation do not have asymptotic bias, regardless of how fast $T$ increases relative to $n$. This conclusion is specific to using the FOD transformation. A similar conclusion does not necessarily apply when other transformations are used to remove fixed effects. Monte Carlo evidence illustrating the analytical results is provided.
