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No 56:
Performance of Some Ridge Parameters for Probit Regression: with Application on Swedish Job Search Data

Håkan Locking, Kristofer Månsson () and Ghazi Shukur

Abstract: In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) using Monte Carlo simulations. In the design of the experiment we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data we also illustrate the benefits of the new method.

Keywords: probit regression; maximum likelihood; multicollinearity; ridge regression; MSE; job search; (follow links to similar papers)

JEL-Codes: C21; (follow links to similar papers)

18 pages, February 16, 2012

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