A Poisson INAR(1) model with serially dependent innovations

Christian Weiß ()

Metrika: International Journal for Theoretical and Applied Statistics, 2015, vol. 78, issue 7, 829-851

Abstract: Motivated by a certain type of infinite-patch metapopulation model, we propose an extension to the popular Poisson INAR(1) model, where the innovations are assumed to be serially dependent in such a way that their mean is increased if the current population is large. We shall recognize that this new model forms a bridge between the Poisson INAR(1) model and the INARCH(1) model. We analyze the stochastic properties of the observations and innovations from an extended Poisson INAR(1) process, and we consider the problem of model identification and parameter estimation. A real-data example about iceberg counts shows how to benefit from the new model. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Count data time series; INAR(1) model; Maximum likelihood estimation; Score test; Overdispersion; State-dependence (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:78:y:2015:i:7:p:829-851

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DOI: 10.1007/s00184-015-0529-9

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