ML and GMM with concentrated instruments in the static panel data model
研究了静态面板模型中工具变量估计量在多个工具变量下的渐近行为，提出了基于集中工具变量的新估计量和标准误，并分析了最大似然估计量的一致性条件。
We study the asymptotic behavior of instrumental variable estimators in the static panel model under many-instruments asymptotics. We provide new estimators and standard errors based on concentrated instruments as alternatives to an estimator based on maximum likelihood. We prove that the latter estimator is consistent under many-instruments asymptotics only if the starting value in an iterative procedure is root-N consistent. A similar approach for continuous updating GMM shows the derivation is nontrivial. For the standard cross-sectional case (T = 1), the simple formulation of standard errors offer an alternative to earlier formulations.