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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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    References listed on IDEAS

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    1. Weiren Wang, 1997. "Semi-parametric estimation of the effect of health on labour force participation of married women," Applied Economics, Taylor & Francis Journals, vol. 29(3), pages 325-329.
    2. Gabler, Siegfried & Laisney, Francois & Lechner, Michael, 1993. "Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 61-80, January.
    3. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    4. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
    5. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    6. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
    7. Ana Fernandez & Juan Rodriquez-Poo, 1997. "Estimation and specification testing in female labor participation models: parametric and semiparametric methods," Econometric Reviews, Taylor & Francis Journals, vol. 16(2), pages 229-247.
    8. Horowitz, Joel L., 1993. "Semiparametric estimation of a work-trip mode choice model," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 49-70, July.
    9. Gerfin, Michael, 1996. "Parametric and Semi-parametric Estimation of the Binary Response Model of Labor Market Participation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 321-339, May-June.
    10. Insik Min & Inchul Kim, 2004. "A Monte Carlo comparison of parametric and nonparametric quantile regressions," Applied Economics Letters, Taylor & Francis Journals, vol. 11(2), pages 71-74.
    11. Stern, Steven, 1996. "Semiparametric estimates of the supply and demand effects of disability on labor force participation," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 49-70.
    12. Heckman, James J, 1993. "What Has Been Learned about Labor Supply in the Past Twenty Years?," American Economic Review, American Economic Association, vol. 83(2), pages 116-121, May.
    13. Justin Tobias, 2003. "The effects of cognitive ability and high school quality on college entry decisions: nonparametric estimation of parameters of interest," Applied Economics, Taylor & Francis Journals, vol. 35(2), pages 209-215.
    14. Mikiyo Kii Niizeki, 1998. "A comparison of short-term interest rate models: empirical tests of interest rate volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 505-512.
    15. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    16. Mikiyo Kii Niizeki, 1998. "Empirical tests of short-term interest rate models: a nonparametric approach," Applied Financial Economics, Taylor & Francis Journals, vol. 8(4), pages 347-352.
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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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