Multivariate count autoregression

Fokianos, Konstantinos and Stove, Bard and Tjostheim, Dag and Doukhan, Paul (2020) Multivariate count autoregression. Bernoulli, 26 (1). pp. 471-499. ISSN 1350-7265

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Abstract

We are studying linear and log-linear models for multivariate count time series data with Poisson marginals. For studying the properties of such processes we develop a novel conceptual framework which is based on copulas. Earlier contributions impose the copula on the joint distribution of the vector of counts by employing a continuous extension methodology. Instead we introduce a copula function on a vector of associated continuous random variables. This construction avoids conceptual difficulties related to the joint distribution of counts yet it keeps the properties of the Poisson process marginally. Furthermore, this construction can be employed for modeling multivariate count time series with other marginal count distributions. We employ Markov chain theory and the notion of weak dependence to study ergodicity and stationarity of the models we consider. Suitable estimating equations are suggested for estimating unknown model parameters. The large sample properties of the resulting estimators are studied in detail. The work concludes with some simulations and a real data example.

Item Type:
Journal Article
Journal or Publication Title:
Bernoulli
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
136567
Deposited By:
Deposited On:
10 Sep 2019 15:45
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Sep 2020 05:08