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Sample Selection Models inR: PackagesampleSelection
Author(s) -
Ott Toomet,
Arne Henningsen
Publication year - 2008
Publication title -
journal of statistical software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.636
H-Index - 145
ISSN - 1548-7660
DOI - 10.18637/jss.v027.i07
Subject(s) - selection (genetic algorithm) , r package , computer science , sample (material) , toolbox , model selection , econometrics , sample size determination , identification (biology) , infinity , statistics , mathematics , machine learning , programming language , mathematical analysis , chemistry , botany , chromatography , biology
This paper describes the implementation of Heckman-type sample selection models in R. We discuss the sample selection problem as well as the Heckman solution to it, and argue that although modern econometrics has non- and semiparametric estimation methods in its toolbox, Heckman models are an integral part of the modern applied analysis and econometrics syllabus. We describe the implementation of these models in the package sampleSelection and illustrate the usage of the package on several simulation and real data examples. Our examples demonstrate the effect of exclusion restrictions, identification at infinity and misspecification. We argue that the package can be used both in applied research and teaching.

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