@article{ref1,
title="A negative binomial model for time series of counts",
journal="Biometrika",
year="2009",
author="Davis, Richard A. and Wu, Rongning",
volume="96",
number="3",
pages="735-749",
abstract="We study generalized linear models for time series of counts, where serial dependence is introduced through a dependent latent process in the link function. Conditional on the covariates and the latent process, the observation is modelled by a negative binomial distribution. To estimate the regression coefficients, we maximize the pseudolikelihood that is based on a generalized linear model with the latent process suppressed. We show the consistency and asymptotic normality of the generalized linear model estimator when the latent process is a stationary strongly mixing process. We extend the asymptotic results to generalized linear models for time series, where the observation variable, conditional on covariates and a latent process, is assumed to have a distribution from a one-parameter exponential family. Thus, we unify in a common framework the results for Poisson log-linear regression models of Davis et al. (2000), negative binomial logit regression models and other similarly specified generalized linear models.<p /><p>Language: en</p>",
language="en",
issn="0006-3444",
doi="10.1093/biomet/asp029",
url="http://dx.doi.org/10.1093/biomet/asp029"
}