A semi-parametric integer-valued autoregressive model with covariates

Rao, Yao ORCID: 0000-0003-1341-3456, Harris, David and McCabe, Brendan ORCID: 0000-0002-9731-1766 (2022) A semi-parametric integer-valued autoregressive model with covariates. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 71 (3). pp. 495-516.

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Official URL: http://dx.doi.org/10.1111/rssc.12543

Abstract

<jats:title>Abstract</jats:title><jats:p>We consider a low count data INAR (Integer Autoregressive Regression) model in which the arrivals are modelled non-parametrically and are allowed to contain covariates. Accommodating possible covariates is important as exogenous variability, such as seasonality, often needs to be catered for. The main challenge is to maintain the axiomatic properties of the arrivals non-parametric mass function while, at the same time, incorporating covariates directly into the associated probabilities. Compared with models that impose standard distributions such as Poisson or Negative Binomial for the arrivals, our approach is more flexible and provides a general arrival specification. The dependence structure is parametric and uses the standard binomial thinning operator. The parameters are estimated by the Maximum Likelihood. Monte Carlo simulations show that our proposed model performs very well with good finite sample results. Two empirical issues are addressed where incorporating covariates is a prerequisite for successful modelling. The first incorporates seasonal covariates into a semi-parametric model for forecasting the numbers of claimants of wage loss benefits in the logging industry in British Columbia, Canada. The second investigates if macro-economic indicators in an economy may be useful in predicting the number of bank failures in the US financial sector.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	count data time-series, covariates, integer autoregressive model, semi-parametric
Depositing User:	Symplectic Admin
Date Deposited:	16 Nov 2022 11:49
Last Modified:	27 Nov 2023 03:35
DOI:	10.1111/rssc.12543
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166214