Scandinavian Working Papers in Economics

HUI Working Papers,
HUI Research

No 42: A Poisson Ridge Regression Estimator

Kristofer Månsson and Ghazi Shukur ()
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Kristofer Månsson: Jönköping University, Postal: Department of Economics and Statistics, Box 1026,, 551 11, Jönköping, Sweden
Ghazi Shukur: Jönköping University, Postal: Department of Economics and Statistics, Jönköping, Sweden, and Department of Economics and Statistics, Linnaeus University, Box 451, 351 06 Växjö, Sweden

Abstract: The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML). 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.

Keywords: Poisson regression; maximum likelihood; ridge regression; MSE; Monte Carlo simulations; Multicollinearity

JEL-codes: C30

16 pages, August 1, 2010

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