Inference for Iterated GMM Under Misspecification
研究了误设定下迭代广义矩方法（GMM）估计量的推断方法，给出了存在性和渐近分布理论，并提出了正确估计渐近方差矩阵的方法，模拟表明该方法在正确设定和轻度至中度误设定下均有效。
This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild misspecification. Moment misspecification causes bias in conventional GMM variance estimators, which can lead to severely oversized hypothesis tests. We show how to consistently estimate the correct asymptotic variance matrix. Our simulation results show that our methods are properly sized under both correct specification and mild to moderate misspecification. We illustrate the method with an application to the model of Acemoglu, Johnson, Robinson, and Yared (2008).