Open Access
2022 Observation-driven models for discrete-valued time series
Mirko Armillotta, Alessandra Luati, Monia Lupparelli
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Electron. J. Statist. 16(1): 1393-1433 (2022). DOI: 10.1214/22-EJS1989


Statistical inference for discrete-valued time series has not been developed like traditional methods for time series generated by continuous random variables. Some relevant models exist, but the lack of a homogenous framework raises some critical issues. For instance, it is not trivial to explore whether models are nested and it is quite arduous to derive stochastic properties which simultaneously hold across different specifications. In this paper, inference for a general class of first order observation-driven models for discrete-valued processes is developed. Stochastic properties such as stationarity and ergodicity are derived under easy-to-check conditions, which can be directly applied to all the models encompassed in the class and for every distribution which satisfies mild moment conditions. Consistency and asymptotic normality of quasi-maximum likelihood estimators are established, with the focus on the exponential family. Finite sample properties and the use of information criteria for model selection are investigated throughout Monte Carlo studies. An empirical application to count data is discussed, concerning a test-bed time series on the spread of an infection.

Funding Statement

This work has been co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation, under the project INFRASTRUCTURES/1216/0017 (IRIDA).


We would like to thank the Editor, the Associate Editor and a Referee for valuable comments and suggestions. We also would like to thank Christian Francq, Kostas Fokianos and David Matteson for their insightful comments to an earlier version of the paper.


Download Citation

Mirko Armillotta. Alessandra Luati. Monia Lupparelli. "Observation-driven models for discrete-valued time series." Electron. J. Statist. 16 (1) 1393 - 1433, 2022.


Received: 1 November 2021; Published: 2022
First available in Project Euclid: 2 March 2022

MathSciNet: MR4387846
zbMATH: 1493.62546
Digital Object Identifier: 10.1214/22-EJS1989

Primary: 62F12 , 62M20
Secondary: 62J12 , 62M10

Keywords: count data , generalized ARMA models , likelihood inference , link-function

Vol.16 • No. 1 • 2022
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