Choosing Between the Sample-Selection Model and the Multi-Part Model
反驳了Hay和Olsen关于多部分模型嵌套于样本选择模型的错误论断，通过反例证明两模型在医疗费用分析中的差异，并指出多部分模型在建模实际结果时更优。
Abstract Hay and Olsen (1984) incorrectly argue that a multi-part model, the two-part model used in Duan et al. (1982,1983), is nested within the sample-selection model. Their proof relies on an unmentioned restrictive assumption that cannot be satisfied. We provide a counterexample to show that the propensity to use medical care and the level of expense can be positively associated in the two-part model, contrary to their assertion. The conditional specification in the multi-part model is preferable to the unconditional specification in the selection model for modeling actual (v. potential) outcomes. The selection model also has poor statistical and numerical properties and relies on untestable assumptions. Empirically the multi-part estimators perform as well as or better than the sample selection estimator for the data set analyzed in Duan et al. (1982, 1983). KEY WORDS: Two-part modelSample selection modelAdjusted Tobit modelSmearing estimateCross validationMean squared forecast error