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Some Ridge Regression estimator for the zero-inflated Poisson model
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (Statistik / LNUC)ORCID iD: 0000-0002-3416-5896
2013 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 40, no 4, 721-735 p.Article in journal (Refereed) Published
Abstract [en]

The zero inflated Poisson regression model is very common when analysing economic data that comes in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression estimators and some methods of estimating the ridge parameter k for the non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error (MSE) and mean absolute error (MAE) are considered as performance criterion. The simulation study shows that some estimators are better than the commonly used maximum likelihood estimator and some other ridge regression estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for the practitioners.

Place, publisher, year, edition, pages
2013. Vol. 40, no 4, 721-735 p.
National Category
Probability Theory and Statistics Economics and Business
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-23179DOI: 10.1080/02664763.2012.752448ISI: 000316390500004Scopus ID: 2-s2.0-84875806498OAI: oai:DiVA.org:lnu-23179DiVA: diva2:580495
Available from: 2012-12-21 Created: 2012-12-21 Last updated: 2017-12-06Bibliographically approved

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Shukur, Ghazi

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