A Small-Sample Estimator for the Sample-Selection Model

Amos Golan, Enrico Moretti and Jeffrey Perloff

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: A semiparametric estimator for evaluating the parameters of data generated under a sample selection process is developed. This estimator is based on the generalized maximum entropy estimator and performs well for small and ill-posed samples. Theoretical and sampling comparisons with parametric and semiparametric estimators are given. This method and standard ones are applied to three small-sample empirical applications of the wage-participation model for female teenage heads of households, immigrants, and Native Americans.

Keywords: maximum entropy; sample selection; Monte Carlo experiments (search for similar items in EconPapers)
Date: 2001-03-01
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Working Paper: A Small-Sample Estimator for the Sample-Selection Model (2001)
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