An Information-Theoretic Alternative to Generalized Method of Moments Estimation
针对广义矩估计在小样本中表现不佳的问题，本文提出一种基于最小化Kullback-Leibler信息准则的计算简单替代方法，适用于弱相关数据，并证明其大样本性质与GMM类似。
While optimally weighted GMM estimation has desirable large sample properties, its small sample performance is poor in some applications. We propose a computationally simple alternative, for weakly dependent data generating mechanisms, based on minimization of the Kullback-Leibler Information Criterion. Conditions are derived under which the large sample properties of this estimator are similar to GMM, i.e., the estimator will be consistent and asymptotically normal, with the same asymptotic covariance matrix as GMM. In addition, we propose overidentifying and parametric restrictions tests as alternatives to analogous GMM procedures.