TY  - JOUR
PY  - 2009//
TI  - A negative binomial model for time series of counts
JO  - Biometrika
A1  - Davis, Richard A.
A1  - Wu, Rongning
SP  - 735
EP  - 749
VL  - 96
IS  - 3
N2  - 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>
LA  - en
SN  - 0006-3444
UR  - http://dx.doi.org/10.1093/biomet/asp029
ID  - ref1
ER  -