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Open AccessFeature PaperArticle

Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Econometrics 2019, 7(4), 43; https://doi.org/10.3390/econometrics7040043
Received: 17 May 2019 / Revised: 23 September 2019 / Accepted: 2 October 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
For modeling count time series data, one class of models is generalized integer autoregressive of order p 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 p 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. View Full-Text
Keywords: count time series; binomial thinning; thinning operators; compounding operation; self-generalized property count time series; binomial thinning; thinning operators; compounding operation; self-generalized property
MDPI and ACS Style

Joe, H. Likelihood Inference for Generalized Integer Autoregressive Time Series Models. Econometrics 2019, 7, 43.

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