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Ridge estimators for probit regression: With an application to labour market data
Department of Economics and Statistics, Linnaeus University, Sweden.
Jönköping University, Jönköping International Business School, JIBS, Statistics.
Jönköping University, Jönköping International Business School, JIBS, Statistics.
2015 (English)In: Bulletin of Economic Research, ISSN 0307-3378, E-ISSN 1467-8586, Vol. 66, no S1, p. S92-S103Article in journal (Refereed) Published
Abstract [en]

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 is 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 MSE than the ML method for all different situations that has been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba (1999) dataset which is based on a labor market experiment.

 

Place, publisher, year, edition, pages
2015. Vol. 66, no S1, p. S92-S103
Keywords [en]
maximum likelihood, Monte Carlo simulations, MSE, multicollinearity, probit regression, ridge regression
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hj:diva-22425DOI: 10.1111/boer.12015ISI: 000348550700006Scopus ID: 2-s2.0-84921598965OAI: oai:DiVA.org:hj-22425DiVA, id: diva2:656717
Available from: 2013-10-16 Created: 2013-10-16 Last updated: 2017-12-06Bibliographically approved

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Månsson, KristoferShukur, Ghazi

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  • nn-NB
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  • Other locale
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Output format
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