A Poisson ridge regression estimator
AbstractThe standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method. The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression estimator (PRR) as a remedy to the problem of instability of the traditional ML method. To investigate the performance of the PRR and the traditional ML approaches for estimating the parameters of the Poisson regression model, we calculate the mean squared error (MSE) using Monte Carlo simulations. The result from the simulation study shows that the PRR method outperforms the traditional ML estimator in all of the different situations evaluated in this paper.
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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 28 (2011)
Issue (Month): 4 (July)
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Web page: http://www.elsevier.com/locate/inca/30411
Poisson regression Maximum likelihood Ridge regression MSE Monte Carlo simulations Multicollinearity;
Other versions of this item:
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Alkhamisi, M.A. & Shukur, Ghazi, 2007. "Developing Ridge Parameters for SUR Models," Working Paper Series in Economics and Institutions of Innovation 80, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
- Månsson, Kristofer & Kibria, B. M. Golam & Sjölander, Pär & Shukur, Ghazi, 2011. "New Liu Estimators for the Poisson Regression Model: Method and Application," HUI Working Papers 51, HUI Research.
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