Bias-Corrected Moment-Based Estimators for Parametric Models Under Endogenous Stratified Sampling
提出一套从内生分层样本中估计参数模型的综合方法，通过多种方式消除随机抽样下矩指标的偏差，得到适用于边际分层概率已知或未知情况的矩估计量，推导简单直观且涵盖已有似然估计量。
This paper provides an integrated approach for estimating parametric models from endogenous stratified samples. We discuss several alternative ways of removing the bias of the moment indicators usually employed under random sampling for estimating the parameters of the structural model and the proportion of the strata in the population. Those alternatives give rise to a number of moment-based estimators that are appropriate for both cases where the marginal strata probabilities are known and unknown. The derivation of our estimators is very simple and intuitive and incorporates as particular cases most of the likelihood-based estimators previously suggested by other authors.