期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 99, 期 -, 页码 51-67出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2016.01.009
关键词
Integer-valued time series; Threshold Poisson autoregressive models; Zero-inflated generalized Poisson INGARCH models; Structural break; MCMC
资金
- Ministry of Science and Technology, Taiwan (MOST) [103-2118-M-035-002-MY2]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT and future Planning [2015R1A2A2A010003894]
To better describe the characteristics of time series of counts such as over-dispersion, asymmetry, structural change, and a large proportion of zeros, this paper considers a class of generalized Poisson autoregressive models that properly capture flexible asymmetric and nonlinear responses through a switching mechanism. We also investigate zero-inflated generalized Poisson autoregressive models with a structural break that can cope with data having a large portion of zeros and changes in dynamics. We employ an adaptive Markov Chain Monte Carlo (MCMC) sampling scheme to locate the structural break and to estimate model parameters. As an illustration, we conduct a simulation study and empirical analysis of New South Wales crime data sets. Our findings show a remarkable improvement by modeling the data based on such generalized Poisson autoregressive models and the Bayesian method. (C) 2016 Elsevier B.V. All rights reserved.
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