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Nonparametric conditional density estimation of labour force participation

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  • Anil Kumar

Abstract

Labour force participation decision has been studied primarily in a parametric framework. The weaknesses of the parametric estimators to misspecification of the error distribution and to functional form assumptions are well known. This paper compares the predictive performance of widely used parametric and semiparametric estimators with results obtained from nonparametric kernel conditional density estimation with likelihood cross-validated bandwidth selection and mixed data type. The results are striking. The predictive performance of the nonparametric estimator is 95% against 71% to 77% of the parametric and semiparametric estimators. The nonparametric estimator is able to correctly predict the outcome for 83% of non-participants in the labour force as against 15% by probit and logit models. This underscores the need to use nonparametric estimators in studying labour market behaviour.

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  • Anil Kumar, 2006. "Nonparametric conditional density estimation of labour force participation," Applied Economics Letters, Taylor & Francis Journals, vol. 13(13), pages 835-841.
    • Handle: RePEc:taf:apeclt:v:13:y:2006:i:13:p:835-841
    DOI: 10.1080/13504850500425204
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    Cited by:

    1. Obbey Elamin & Len Gill & Martyn Andrews, 2020. "Insights from kernel conditional-probability estimates into female labour force participation decision in the UK," Empirical Economics, Springer, vol. 58(6), pages 2981-3006, June.

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