Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both n and T Are Large
研究n和T都很大时固定效应动态面板AR(1)模型的推断问题，提出对最大似然估计进行简单修正，得到渐近无偏且有效的估计量。
We consider a dynamic panel AR(1) model with fixed effects when both n and T are large.Under the "T fixed n large" asymptotic approximation, the maximum likelihood estimator is known to be inconsistent due to the well-known incidental peirameter problem.We consider an alternative asymptotic approximation where n and T grow at the same rate.It is shown that, although the MLE is asymptotically biased, a relatively simple fix to the MLE results in an asymptotically unbiased estimator.The bias corrected MLE is shown to be asymptotically efficient by a Hajek type convolution theorem.