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


Physical Sciences and Mathematics | Statistics and Probability


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.





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