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Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Harry Joe ()
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Harry Joe: Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

Econometrics, 2019, vol. 7, issue 4, 1-13

Abstract: 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.

Keywords: count time series; binomial thinning; thinning operators; compounding operation; self-generalized property (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:gam:jecnmx:v:7:y:2019:i:4:p:43-:d:275407