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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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| Published in: | Computational statistics & data analysis 2016-07, Vol.99, p.51-67 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c333t-ee2352462036d5b1e815e9c8b462ce27f77e3aaecad8d98de00472dbf1aafb373 |
| container_end_page | 67 |
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| container_title | Computational statistics & data analysis |
| container_volume | 99 |
| creator | Chen, Cathy W.S. Lee, Sangyeol |
| description | 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. |
| doi_str_mv | 10.1016/j.csda.2016.01.009 |
| format | article |
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| ispartof | Computational statistics & data analysis, 2016-07, Vol.99, p.51-67 |
| issn | 0167-9473 1872-7352 |
| language | eng |
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| source | ScienceDirect Freedom Collection 2022-2024; Backfile Package - Computer Science (Legacy) [YCS]; Backfile Package - Mathematics (Legacy) [YMT]; Backfile Package - Decision Sciences [YDT] |
| 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 |
| title | Generalized Poisson autoregressive models for time series of counts |
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