Negative binomial quasi-likelihood inference for general integer-valued time series models
Abdelhakim Aknouche and
MPRA Paper from University Library of Munich, Germany
Two negative binomial quasi-maximum likelihood estimates (NB-QMLE's) for a general class of count time series models are proposed. The first one is the profile NB-QMLE calculated while arbitrarily fixing the dispersion parameter of the negative binomial likelihood. The second one, termed two-stage NB-QMLE, consists of four stages estimating both conditional mean and dispersion parameters. It is shown that the two estimates are consistent and asymptotically Gaussian under mild conditions. Moreover, the two-stage NB-QMLE enjoys a certain asymptotic efficiency property provided that a negative binomial link function relating the conditional mean and conditional variance is specified. The proposed NB-QMLE's are compared with the Poisson QMLE asymptotically and in finite samples for various well-known particular classes of count time series models such as the (Poisson and negative binomial) Integer GARCH model and the INAR(1) model. Applications to two real datasets are given.
Keywords: Integer-valued time series models; Integer GARCH; Integer AR; Generalized Linear Models; Quasi-likelihood; Geometric QMLE; Negative Binomial QMLE; Poisson QMLE; consistency and asymptotic normality. (search for similar items in EconPapers)
JEL-codes: C01 C13 C18 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2016-12-06, Revised 2017-02-03
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https://mpra.ub.uni-muenchen.de/83082/8/MPRA_paper_83082.pdf revised version (application/pdf)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:76574
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