RIDGE ESTIMATORS FOR PROBIT REGRESSION: WITH AN APPLICATION TO LABOUR MARKET DATA

Authors

  • Håkan Locking,

    1. Department of Economics and Statistics, Linnaeus University, Sweden
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  • Kristofer Månsson,

    1. Department of Economics, Finance and Statistics, Jönköping University, Sweden
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  • Ghazi Shukur

    Corresponding author
    1. Department of Economics and Statistics, Linnaeus University, Sweden
    2. Department of Economics, Finance and Statistics, Jönköping University, Sweden
    • Correspondence: Ghazi Shukur, Department of Economics, Finance and Statistics, Jönköping International Business School, P.O. Box 1026 SE-551, Jönköping, Sweden. Tel: 0046-36-101763; Email: ghazi.shukur@jibs.hj.se.

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ABSTRACT

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.

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