Projecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics
提出两种新方法，利用倾向性评分对前期数据进行重新加权，使估计结果更接近最终数据集的估计值。以1982年税收申报数据为例，新方法比传统方法更准确，适用于无回答调整、覆盖不足修正和统计匹配等问题。
This article proposes and evaluates two new methods of reweighting preliminary data to obtain estimates more closely approximating those derived from the final data set. In our motivating example, the preliminary data are an early sample of tax returns, and the final data set is the sample after all tax returns have been processed. The new methods estimate a predicted propensity for late filing for each return in the advance sample and then poststratify based on these propensity scores. Using advance and complete sample data for 1982, we demonstrate that the new methods produce advance estimates generally much closer to the final estimates than those derived from the current advance estimation techniques. The results demonstrate the value of propensity modeling, a general-purpose methodology that can be applied to a wide range of problems, including adjustment for unit nonresponse and frame undercoverage as well as statistical matching.