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arXiv · 1509.08666

Bayesian GARMA Models for Count Data

Marinho G. Andrade·Ricardo S. Ehlers·Breno S. Andrade
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Abstract

Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents Bayesian approach for GARMA models with Poisson, binomial and negative binomial distributions. A simulation study was carried out to investigate the performance of Bayesian estimation and Bayesian model selection criteria. Also three real datasets were analysed using the Bayesian approach on GARMA models.

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Marinho G. Andrade, Ricardo S. Ehlers, Breno S. Andrade. 2015-09-29. Bayesian GARMA Models for Count Data. https://doi.org/10.1080/23737484.2016.1190307

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