Bayesian modelling of nonlinear negative binomial integer-valued GARCHX models. (December 2020)

• Record Type: Journal Article
• Authors: Chen, Cathy WS; Khamthong, K
• Is Part Of: Statistical modelling. Volume 20:Number 6(2020)
• Page Start: 537
• Page End: 561
• Publication Date: 2020-12
• Subjects: Dengue fever -- Integer-valued time series -- Markov chain Monte Carlo method -- Markov switching -- consecutive zeros -- meteorological covariates; 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: 10.1177/1471082X19845541
• Languages: English
• ISSNs: 1471-082X

Abstract

This study focuses on modelling dengue cases in northeastern Thailand through two meteorological covariates: cumulative rainfall and average maximum temperature. We propose two nonlinear integer-valued GARCHX models (Markov switching and threshold specification) with a negative binomial distribution, as they take into account the stylized features of weekly dengue haemorrhagic fever cases, which contain nonlinear dynamics, lagged dependence, overdispersion, consecutive zeros and asymmetric effects of meteorological covariates. We conduct parameter estimation and one-step-ahead forecasting for two proposed models based on Bayesian Markov chain Monte Carlo (MCMC) methods. A simulation study illustrates that the adaptive MCMC sampling scheme performs well. The empirical results offer strong support for the Markov switching integer-valued GARCHX model over its competitors via Bayes factor and deviance information criterion. We also provide one-step-ahead forecasting based on the prediction interval that offers a useful early warning signal of outbreak detection.