On ridge estimators for the negative binomial regression model

Kristofer Månsson ()

Economic Modelling, 2012, vol. 29, issue 2, 178-184

Abstract: The negative binomial (NB) regression model is very popular in applied research when analyzing count data. The commonly used maximum likelihood (ML) estimator is very sensitive to highly intercorrelated explanatory variables. Therefore, a NB ridge regression estimator (NBRR) is proposed as a robust option of estimating the parameters of the NB model in the presence of multicollinearity. To investigate the performance of the NBRR and the traditional ML approach the mean squared error (MSE) is calculated using Monte Carlo simulations. The simulated result indicated that some of the proposed NBRR methods should always be preferred to the ML method.

Keywords: Negative binomial regression; Maximum likelihood; Ridge regression; MSE; Monte Carlo simulations; Multicollinearity (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:29:y:2012:i:2:p:178-184

DOI: 10.1016/j.econmod.2011.09.009

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