Statistical Modelling 20 (6) (2020), 537–561
Bayesian modelling of nonlinear negative binomial integer-valued GARCHX models
Cathy WS Chen,
Department of Statistics,
Feng Chia University,
Taichung, Taiwan,
R.O.C.
e-mail: chenws@mail.fcu.edu.tw
K Khamthong,
Cathy WS Chen,
Department of Statistics,
Feng Chia University,
Taichung, Taiwan,
R.O.C.
and
Department of Mathematics,
Mahasarakham University,
Thailand.
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.
Keywords:
Dengue fever, Integer-valued time series, Markov chain Monte Carlo method, Markov switching, consecutive zeros, meteorological covariates.
Downloads:
Example data and code in zipped archive.
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