Finite-Sample Properties of the Maximum Likelihood Estimator for the Poisson Regression Model With Random Covariates
Qian Chen and
David Giles ()
No 907, Econometrics Working Papers from Department of Economics, University of Victoria
We examine the small-sample behaviour of the maximum likelihood estimator for the Poisson regression model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error for this estimator are derived, and we undertake some numerical evaluations to illustrate these results for the single covariate case. The properties of the bias-adjusted maximum likelihood estimator, constructed by subtracting the estimated first-order bias from the original estimator, are investigated in a Monte Carlo experiment. Correcting the estimator for its first-order bias is found to be effective in the cases considered, and we recommend its use when the Poisson regression model is estimated by maximum likelihood with small samples.
Keywords: Poisson regression model; bias; mean squared error; bias correction; random covariates (search for similar items in EconPapers)
JEL-codes: C01 C13 C25 (search for similar items in EconPapers)
Pages: 17 pages
New Economics Papers: this item is included in nep-ecm
Note: ISSN 1485-6441
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0907
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