AVERAGING OF AN INCREASING NUMBER OF MOMENT CONDITION ESTIMATORS
研究了一类由多个非线性估计量线性组合而成的估计量，其个数随样本量增加，证明了其一致性和渐近正态性，并讨论了最优加权问题。
We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of $\sqrt n$ -consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achieves similar objectives of aggregating the available information. One advantage of aggregating the estimators rather than the moment conditions is that it yields robustness to certain types of parameter heterogeneity in the sense that it delivers consistent estimates of the mean effect in that case. We discuss the question of optimal weighting of the estimators.