Integer valued AR processes with explanatory variables

Enciso-Mora, Victor and Neal, Peter John and Subba Rao, Tata (2009) Integer valued AR processes with explanatory variables. Sankyha B : Applied and Interdisciplinary Statistics, 71 (2). pp. 248-263. ISSN 0976-8394

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Abstract

Integer valued AR (INAR) processes are perfectly suited for modelling count data. We consider the inclusion of explanatory variables into the INAR model to extend the applicability of INAR models. This greatly extends the range of time series data sets to which INAR models can be applied and offers an alternative to Poisson regression models. An efficient MCMC algorithm is constructed to analyze the model and incorporates both explanatory variable and order selection. The applicability of the methodology is demonstrated by considering three different data sets; monthly polio incidences in the USA 1970-1983, monthly benefit claims from the logging industry to the British Columbia Workers’ Compensation Board 1985-1994 and the daily score achieved by a schizophrenic patient in a test of perceptual speed.

Item Type:
Journal Article
Journal or Publication Title:
Sankyha B : Applied and Interdisciplinary Statistics
ID Code:
76917
Deposited By:
Deposited On:
27 Nov 2015 13:54
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Mar 2020 04:05