Model selection, estimation and forecasting in INAR(p) models:: A likelihood-based Markov Chain approach

Bu, Ruijun ORCID: 0000-0002-3947-3038 and McCabe, Brendan ORCID: 0000-0002-9731-1766 (2008) Model selection, estimation and forecasting in INAR(p) models:: A likelihood-based Markov Chain approach INTERNATIONAL JOURNAL OF FORECASTING, 24 (1). pp. 151-162. ISSN 0169-2070

Text Bu and McCabe (2008) Coherent Forecasting with INAR(p).pdf - Author Accepted Manuscript Download (465kB)

Abstract

This paper considers model selection, estimation and forecasting for a class of integer autoregressive models suitable for use when analysing time series count data. Any number of lags may be entertained, and estimation may be performed by likelihood methods. Model selection is enhanced by the use of new residual processes that are defined for each of the p + 1 unobserved components of the model. Forecasts are produced by treating the model as a Markov Chain, and estimation error is accounted for by providing confidence intervals for the probabilities of each member of the support of the count data variable. Confidence intervals are also available for more complicated event forecasts such as functions of the cumulative distribution function, e.g., for probabilities that the future count will exceed a given threshold. A data set of Australian counts on medical injuries is analysed in detail. © 2007.

Item Type:	Article
Additional Information:	## TULIP Type: Articles/Papers (Journal) ##
Uncontrolled Keywords:	time series of counts, INAR(p) models, Maximum Likelihood estimation, Markov Chain, transition probability, transition matrix, delta method
Depositing User:	Symplectic Admin
Date Deposited:	17 Oct 2016 09:34
Last Modified:	22 May 2026 20:28
DOI:	10.1016/j.ijforecast.2007.11.002
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3003790
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