IDEAS home Printed from https://ideas.repec.org/a/bla/buecrs/v66y2014is1ps92-s103.html
   My bibliography  Save this article

Ridge Estimators For Probit Regression: With An Application To Labour Market Data

Author

Listed:
  • Håkan Locking
  • Kristofer Månsson
  • Ghazi Shukur

Abstract

type="main"> In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.

Suggested Citation

  • Håkan Locking & Kristofer Månsson & Ghazi Shukur, 2014. "Ridge Estimators For Probit Regression: With An Application To Labour Market Data," Bulletin of Economic Research, Wiley Blackwell, vol. 66(S1), pages 92-103, December.
    • Handle: RePEc:bla:buecrs:v:66:y:2014:i:s1:p:s92-s103
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/boer.12015
    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.

    References listed on IDEAS

    as
    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baird, Matthew D. & Engberg, John & Gutierrez, Italo A., 2022. "RCT evidence on differential impact of US job training programmes by pre-training employment status," Labour Economics, Elsevier, vol. 75(C).
    2. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    3. David Card, 2022. "Design-Based Research in Empirical Microeconomics," American Economic Review, American Economic Association, vol. 112(6), pages 1773-1781, June.
    4. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    5. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
    6. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    7. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    8. Rajeev Dehejia, 2013. "The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics," WIDER Working Paper Series wp-2013-011, World Institute for Development Economic Research (UNU-WIDER).
    9. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    10. Sergio Afcha & Jose García-Quevedo, 2016. "The impact of R&D subsidies on R&D employment composition," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 25(6), pages 955-975.
    11. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    12. Burt S. Barnow & Jeffrey Smith, 2015. "Employment and Training Programs," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 127-234, National Bureau of Economic Research, Inc.
    13. Monica Beuran & Gaël Raballand & Julio Revilla, 2011. "Improving Aid Effectiveness in Aid-Dependent Countries: Lessons from Zambia," Documents de travail du Centre d'Economie de la Sorbonne 11040, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    14. Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
    15. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 7(1), pages 71-83, February.
    16. Augurzky, Boris & Kluve, Jochen, 2004. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 21, RWI - Leibniz-Institut für Wirtschaftsforschung.
    17. Aki Kangasharju, 2007. "Do Wage Subsidies Increase Employment in Subsidized Firms?," Economica, London School of Economics and Political Science, vol. 74(293), pages 51-67, February.
    18. Tymon Słoczyński, 2015. "The Oaxaca–Blinder Unexplained Component as a Treatment Effects Estimator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 588-604, August.
    19. Hämäläinen, Kari & Ollikainen, Virve, 2004. "Differential Effects of Active Labour Market Programmes in the Early Stages of Young People's Unemployment," Research Reports 115, VATT Institute for Economic Research.
    20. Yusuf Soner Baskaya & Timur Hulagu, 2011. "Informal-Formal Worker Wage Gap in Turkey : Evidence From A Semi-Parametric Approach," Working Papers 1115, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

    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:bla:buecrs:v:66:y:2014:i:s1:p:s92-s103. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0307-3378 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.