Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Likelihood Inference for Generalized Integer Autoregressive Time Series Models

For modeling count time series data, one class of models is generalized integer autoregressive of order <i>p</i> based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distributi...

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Bibliographic Details
Main Author: Harry Joe
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Econometrics
Subjects:
count time series
binomial thinning
thinning operators
compounding operation
self-generalized property
Online Access:https://www.mdpi.com/2225-1146/7/4/43
Description
Summary:For modeling count time series data, one class of models is generalized integer autoregressive of order <i>p</i> based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past <i>p</i> observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.
ISSN:2225-1146

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