Of Butterflies and Caterpillars: Bivariate Normality in the Sample Selection Model
Since the seminal paper by Heckman (1974), the sample selection model has been an essential tool for applied economists and arguably the most sensitive to sources of misspecification among the standard microeconometric models involving limited dependent variables. The need for alternative methods to get consistent estimates has led to a number of estimation proposals for the sample selection model under non-normality. There is a marked dichotomy in the literature that has developed in two conceptually different directions: the bivariate normality assumption can be either replaced, by using copulae, or relaxed/removed, relying on semi and nonparametric estimators. This paper surveys the more recent proposals on the estimation of sample selection model that deal with distributional misspecification giving the practitioner a unified framework of both parametric and semi-nonparametric options.
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