MOMENT-BASED INFERENCE WITH STRATIFIED DATA
研究了在三种常用分层抽样方案下，如何对矩条件模型进行有效的半参数推断，避免因忽略抽样设计导致的估计偏误。
Many data sets used by economists and other social scientists are collected by stratified sampling. The sampling scheme used to collect the data induces a probability distribution on the observed sample that differs from the target or underlying distribution for which inference is to be made. If this effect is not taken into account, subsequent statistical inference can be seriously biased. This paper shows how to do efficient semiparametric inference in moment restriction models when data from the target population are collected by three widely used sampling schemes: variable probability sampling, multinomial sampling, and standard stratified sampling.