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A restricted Liu estimator for binary regression models and its application to an applied demand system
Jönköping University.
Florida International University, USA.
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University. (Statistics)ORCID iD: 0000-0002-3416-5896
2016 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 43, no 6, 1119-1127 p.Article in journal (Refereed) Published
Abstract [en]

In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is suspected that β may belong to a linear subspace defined by =r. First, we investigate the mean squared error (MSE) properties of the new estimator and compare them with those of the restricted maximum likelihood estimator (RMLE). Then we suggest some estimators of the shrinkage parameter, and a simulation study is conducted to compare the performance of the different estimators. Finally, we show the benefit of using RLRE instead of RMLE when estimating how changes in price affect consumer demand for a specific product.

Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 43, no 6, 1119-1127 p.
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics; Economy
Identifiers
URN: urn:nbn:se:lnu:diva-46116DOI: 10.1080/02664763.2015.1092110ISI: 000371182400009Scopus ID: 2-s2.0-84958944234OAI: oai:DiVA.org:lnu-46116DiVA: diva2:851560
Available from: 2015-09-06 Created: 2015-09-06 Last updated: 2016-12-14Bibliographically approved

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Shukur, Ghazi
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CiteExportLink to record
Permanent link

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Cite
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
  • rtf