On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods. (February 2023)

• Record Type: Journal Article
• Authors: Tsamtsakiri, Panagiota; Karlis, Dimitris
• Is Part Of: Statistical modelling. Volume 23:Number 1(2023)
• Page Start: 81
• Page End: 98
• Publication Date: 2023-02
• Subjects: INGARCH models -- Observation-driven model -- trans-dimensional Markov chain Monte Carlo -- pseudopriors; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X211034705
• Languages: English
• ISSNs: 1471-082X

Abstract

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.