•  
  •  
 

Keywords

Count time series, over-dispersion, stationary, binomial thinning, and Poisson-Akash innovation

Disciplines

Physical Sciences and Mathematics | Statistics and Probability

Abstract

In this paper, a Poisson-Akash INAR(1) model was proposed in order to improve on the modelling of overdispersed stationary count time series. The estimators of the parameters of the model were derived using the Yule-Walker (YW) method and the conditional least squares (CLS) method. An expression for the conditional log-likelihood and the r-step ahead forecast were obtained for the model. Three overdispersed nonseasonal stationary count time series were modelled to illustrate the applicability of the proposed model as well as its capacity to outperform the competing INAR (1) models in modelling overdispersed stationary count time series and the result showed that the proposed model is a strong competitor in the analysis of overdispersed stationary count time series and can perform better than the competing INAR(1) models for some data sets.

ISSN

2706-784X

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.