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Sample Selection Models in R: Package sampleSelection

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  • Ott Toomet
  • Arne Henningsen

Abstract

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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Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 27 ()
Issue (Month): i07 ()
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Handle: RePEc:jss:jstsof:27:i07

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  1. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
  2. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
  3. Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
  4. Arne Henningsen & Jeff D. Hamann, . "systemfit: A Package for Estimating Systems of Simultaneous Equations in R," Journal of Statistical Software, American Statistical Association, vol. 23(i04).
  5. Thomas Mroz, . "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," University of Chicago - Population Research Center 84-8, Chicago - Population Research Center.
  6. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  7. Calzolari, Giorgio & Fiorentini, Gabriele, 1993. "Alternative covariance estimators of the standard Tobit model," Economics Letters, Elsevier, vol. 42(1), pages 5-13.
  8. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc.
  9. Heckman, James J. & Macurdy, Thomas E., 1986. "Labor econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 32, pages 1917-1977 Elsevier.
  10. Greene, William H, 1981. "Sample Selection Bias as a Specification Error: Comment," Econometrica, Econometric Society, vol. 49(3), pages 795-98, May.
  11. Chamberlain, Gary, 1986. "Asymptotic efficiency in semi-parametric models with censoring," Journal of Econometrics, Elsevier, vol. 32(2), pages 189-218, July.
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Cited by:
  1. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
  2. Romain Aeberhardt & Denis Fougère & Julien Pouget & Roland Rathelot, 2007. "Wages and Employment of French Workers with African Origin," Working Papers 2007-36, Centre de Recherche en Economie et Statistique.
  3. Fabien Bocart & Kim Oosterlinck, 2011. "Discoveries of fakes: their impact on the art market," Working Papers CEB 11-023, ULB -- Universite Libre de Bruxelles.
  4. Biørn, Erik & Wangen, Knut R., 2012. "New Taxonomies for Limited Dependent Variables Models," MPRA Paper 41461, University Library of Munich, Germany.
  5. Ding, Peng, 2014. "Bayesian robust inference of sample selection using selection-t models," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 451-464.

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