Nonparametric conditional density estimation of labour force participation
AbstractLabour 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Economics Letters.
Volume (Year): 13 (2006)
Issue (Month): 13 ()
Contact details of provider:
Web page: http://www.tandfonline.com/RAEL20
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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 321-39, May-June.
- Michael Gerfin, 1993. "Parametric and Semiparametric Estimation of the Binary Response Model of Labor Market Participation," Diskussionsschriften dp9315, Universitaet Bern, Departement Volkswirtschaft.
- 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.
- 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.
- 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.
- Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
- 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 116-21, May.
- 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.
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.