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A Poisson ridge regression estimator


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  • Månsson, Kristofer
  • Shukur, Ghazi


The 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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Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 28 (2011)
Issue (Month): 4 (July)
Pages: 1475-1481

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Handle: RePEc:eee:ecmode:v:28:y:2011:i:4:p:1475-1481

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Keywords: Poisson regression Maximum likelihood Ridge regression MSE Monte Carlo simulations Multicollinearity;

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  1. 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.
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Cited by:
  1. 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.
  2. Månsson, Kristofer, 2012. "On ridge estimators for the negative binomial regression model," Economic Modelling, Elsevier, vol. 29(2), pages 178-184.


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