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Ridge estimators for probit regression: with an application to labour market data
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (Ekonometri)
Jönköping University. (Statistik)
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University. (Statistik)ORCID iD: 0000-0002-3416-5896
2014 (English)In: Bulletin of Economic Research, ISSN 0307-3378, E-ISSN 1467-8586, Vol. 66, no S1, S92-S103 p.Article 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
John Wiley & Sons, 2014. Vol. 66, no S1, S92-S103 p.
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-29660DOI: 10.1111/boer.12015ISI: 000348550700006Scopus ID: 2-s2.0-84921598965OAI: oai:DiVA.org:lnu-29660DiVA: diva2:656714
Available from: 2013-10-16 Created: 2013-10-16 Last updated: 2017-12-06Bibliographically approved

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Locking, HåkanShukur, Ghazi

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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