Generalized Poisson autoregressive models for time series of counts

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...

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Bibliographic Details
Published in:Computational statistics & data analysis 2016-07, Vol.99, p.51-67
Main Authors: Chen, Cathy W.S., Lee, Sangyeol
Format: Article
Language:English
Subjects:
Asymmetry
Autoregressive processes
Breaking
Computer simulation
Counting
Integer-valued time series
Mathematical models
MCMC
Monte Carlo methods
Structural break
Threshold Poisson autoregressive models
Time series
Zero-inflated generalized Poisson INGARCH models
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Summary: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.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2016.01.009