Generalized Method of Moments With Many Weak Moment Conditions
针对多弱矩条件下广义矩方法推断不准确的问题，提出了广义经验似然的新方差估计量，并验证了相关检验统计量的有效性，蒙特卡洛模拟显示新方法在多种情形下表现良好。
Using many moment conditions can improve efficiency but makes the usual generalized method of moments (GMM) inferences inaccurate. Two-step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias, but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and, under many weak moment asymptotics, is larger than usual and is consistent. We also show that the Kleibergen (2005) Lagrange multiplier and conditional likelihood ratio statistics are valid under many weak moments. In addition, we introduce a jackknife GMM estimator, but find that GEL is asymptotically more efficient under many weak moments. In Monte Carlo examples we find that t-statistics based on the new variance estimator have nearly correct size in a wide range of cases. Copyright 2009 The Econometric Society.