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

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  • 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
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    References listed on IDEAS

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    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.
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