HUI Working Papers, HUI Research
Kristofer Månsson and Ghazi Shukur
A Poisson Ridge Regression Estimator
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; (follow links to similar papers)
JEL-Codes: C30; (follow links to similar papers)
16 pages, August 1, 2010
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