IDEAS home Printed from https://ideas.repec.org/a/ect/emjrnl/v9y2006i3p511-540.html
   My bibliography  Save this article

Non-parametric regression for binary dependent variables

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2006.00196.x
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cerquera, Daniel & Laisney, François & Ullrich, Hannes, 2012. "Considerations on partially identified regression models," ZEW Discussion Papers 12-024, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    2. 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.
    3. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
    4. 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.
    5. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:511-540. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/resssea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.