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Non-parametric regression for binary dependent variables

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  • Markus Frölich

Abstract

Finite-sample properties of non-parametric regression for binary dependent variables are analyzed. Non parametric regression is generally considered as highly variable in small samples when the number of regressors is large. In binary choice models, however, it may be more reliable since its variance is bounded. The precision in estimating conditional means as well as marginal effects is investigated in settings with many explanatory variables (14 regressors) and small sample sizes (250 or 500 observations). The Klein-Spady estimator, Nadaraya-Watson regression and local linear regression often perform poorly in the simulations. Local likelihood logit regression, on the other hand, is 25 to 55% more precise than parametric regression in the Monte Carlo simulations. In an application to female labour supply, local logit finds heterogeneity in the effects of children on employment that is not detected by parametric or semiparametric estimation. (The semiparametric estimator actually leads to rather similar results as the parametric estimator.) Copyright Royal Economic Society 2006

Suggested Citation

  • Markus Frölich, 2006. "Non-parametric regression for binary dependent variables," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 511-540, November.
    • Handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:511-540
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    Cited by:

    1. Michael Lechner & Blaise Melly, 2007. "Earnings Effects of Training Programs," University of St. Gallen Department of Economics working paper series 2007 2007-28, Department of Economics, University of St. Gallen.
    2. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    3. Lixin Cai & Amy Y.C. Liu, 2008. "Public-Private Wage Gap in Australia: Variation Along the Distribution," CEPR Discussion Papers 581, Centre for Economic Policy Research, Research School of Economics, Australian National University.
    4. Byeong Park & Léopold Simar & Valentin Zelenyuk, 2015. "Categorical data in local maximum likelihood: theory and applications to productivity analysis," Journal of Productivity Analysis, Springer, vol. 43(2), pages 199-214, April.
    5. Cerquera, Daniel & Laisney, François & Ullrich, Hannes, 2012. "Considerations on partially identified regression models," ZEW Discussion Papers 12-024, ZEW - Leibniz Centre for European Economic Research.
    6. Pedro H. C. Sant'Anna & Qi Xu, 2023. "Difference-in-Differences with Compositional Changes," Papers 2304.13925, arXiv.org, revised Jan 2025.
    7. Daniel Cerquera & François Laisney & Hannes Ullrich, 2014. "A Note on Regressions with Interval Data on a Regressor," Discussion Papers of DIW Berlin 1419, DIW Berlin, German Institute for Economic Research.
    8. Lixin Cai & Amy Y. C. Liu, 2011. "Public–Private Sector Wage Gap in Australia: Variation along the Distribution," British Journal of Industrial Relations, London School of Economics, vol. 49(2), pages 362-390, June.
    9. Mathieu David & Joaquín Alonso-Montesinos & Josselin Le Gal La Salle & Philippe Lauret, 2023. "Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera," Energies, MDPI, vol. 16(20), pages 1-18, October.
    10. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.

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