Sample Selection Models in R: Package sampleSelection. 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.
Keywords for this software
References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Zhao, Jun; Kim, Hea-Jung; Kim, Hyoung-Moon: New EM-type algorithms for the Heckman selection model (2020)
- Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
- Marra, Giampiero; Radice, Rosalba: A joint regression modeling framework for analyzing bivariate binary data in (\mathsfR) (2017)
- M. Wojtys; Giampiero Marra; Rosalba Radice: Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel (2016) not zbMATH
- Ogundimu, Emmanuel O.; Hutton, Jane L.: A sample selection model with skew-normal distribution (2016)
- Ding, Peng: Bayesian robust inference of sample selection using selection-(t) models (2014)
- Marra, Giampiero; Radice, Rosalba: Estimation of a regression spline sample selection model (2013)
- Henningsen, Arne; Toomet, Ott: maxLik: a package for maximum likelihood estimation in R (2011)
- Achim Zeileis; Roger Koenker: Econometrics in R: Past, Present, and Future (2008) not zbMATH
- Kleiber, Christian; Zeileis, Achim: Applied econometrics with R (2008)
- Ott Toomet; Arne Henningsen: Sample Selection Models in R: Package sampleSelection (2008) not zbMATH