Determination of Estimators with Minimum Asymptotic Covariance Matrices
给出了一个直接的条件，用于在计量经济学相关的估计量类中确定最小渐近方差估计量，适用于光滑或非光滑目标函数的极值估计，且不要求收敛速度为n^{-1/2}。
We give a straightforward condition sufficient for determining the minimum asymptotic variance estimator in certain classes of estimators relevant to econometrics. These classes are relatively broad, as they include extremum estimation with smooth or nonsmooth objective functions; also, the rate of convergence to the asymptotic distribution is not required to be n −½ . We present examples illustrating the content of our result. In particular, we apply our result to a class of weighted Huber estimators, and obtain, among other things, analogs of the generalized least-squares estimator for least L p -estimation, 1 ≤ p < ∞.