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Bivariate Non-Normality in the Sample Selection Model

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  • Pigini Claudia

    (Department of Economics, University of Perugia, Via A. Pascoli 20 – Perugia, Italy)

Abstract

Since the seminal paper by [Heckman, J. J. 1974. “Shadow Prices, Market Wages, and Labor Supply.” Econometrica 42: 679–694], 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 estimators 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 non-parametric estimators. This paper surveys the more recent proposals on the estimation of the sample selection model that deal with distributional misspecification giving the practitioner a unified framework of both parametric and semi/non-parametric options.

Suggested Citation

  • Pigini Claudia, 2015. "Bivariate Non-Normality in the Sample Selection Model," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 1-22, January.
  • Handle: RePEc:bpj:jecome:v:4:y:2015:i:1:p:22:n:5
    DOI: 10.1515/jem-2013-0008
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    3. Giulia Bettin & Riccardo Lucchetti & Claudia Pigini, 2016. "State dependence and unobserved heterogeneity in a double hurdle model for remittances: evidence from immigrants to Germany," Mo.Fi.R. Working Papers 127, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    4. Kossova, Elena & Kupriianova, Liubov & Potanin, Bogdan, 2020. "Parametric and semiparametric multivariate sample selection models estimators’ accuracy: Comparative analysis on simulated data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 119-139.
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    6. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).

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