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Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data
Department of Economics and Statistics, Centre for Labour Market and Discrimination Studies, Linnaeus University, Växjö, Sweden.
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
2013 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 3, 698-710 p.Article in journal (Refereed) Published
Abstract [en]

In ridge regression, the estimation of the ridge parameter is an important issue. This article 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 is judged by calculating the mean squared 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.

Place, publisher, year, edition, pages
2013. Vol. 42, no 3, 698-710 p.
Keyword [en]
Job search, Maximum likelihood, MSE, Multicollinearity, Probit regression, Ridge regression
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hj:diva-20146DOI: 10.1080/03610918.2011.654032OAI: oai:DiVA.org:hj-20146DiVA: diva2:580631
Available from: 2012-12-23 Created: 2012-12-23 Last updated: 2013-01-08Bibliographically approved

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Citation style
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