Momentary Lapses: Moment Expansions and the Robustness of Minimum Distance Estimation
研究了最小距离（GMM）估计量的稳健性，重点分析中间协方差矩阵估计对最终估计量性能的影响，发现对于峰度足够大的误差分布，Eicker-White协方差矩阵估计具有奇特的稳健化效应。
This paper explores the robustness of minimum distance (GMM) estimators focusing particularly on the effect of intermediate covariance matrix estimation on final estimator performance. Asymptotic expansions to order O p ( n −3/2 ) are employed to construct O ( n −2 ) expansions for the variance of estimators constructed from preliminary least-squares and general M -estimators. In the former case, there is a rather curious robustifying effect due to estimation of the Eicker-White covariance matrix for error distributions with sufficiently large kurtosis.