Model selection for time series of count data
Computational Statistics and Data Analysis
Elsevier for International Association for Statistical Computing
© 2018 Elsevier B.V. All rights reserved.
Reason for embargo
Under embargo until 11 January 2019 in compliance with publisher policy
Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. An effective algorithm is developed in a Bayesian framework for selecting between a parameter-driven autoregressive Poisson regression model and an observationdriven integer valued autoregressive model when modeling time series count data. In order to achieve this a particle MCMC algorithm for the autoregressive Poisson regression model is introduced. The particle filter underpinning the particle MCMC algorithm plays a key role in estimating the marginal likelihood of the autoregressive Poisson regression model via importance sampling and is also utilised to estimate the DIC. The performance of the model selection algorithms are assessed via a simulation study. Two real-life data sets, monthly US polio cases (1970-1983) and monthly benefit claims from the logging industry to the British Columbia Workers Compensation Board (1985-1994) are successfully analysed.
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record
Published online 11 January 2018