GMM with Weak Identification
推导了参数弱识别时GMM估计量和检验统计量的渐近分布理论，应用于线性工具变量回归和CCAPM的欧拉方程估计，提出对弱识别稳健的置信集，并用实证分析展示了与传统方法的差异。
This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak-identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.