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Likelihood Inference for Generalized Integer Autoregressive Time Series Models

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  • Harry Joe

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

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.

Suggested Citation

  • Harry Joe, 2019. "Likelihood Inference for Generalized Integer Autoregressive Time Series Models," Econometrics, MDPI, vol. 7(4), pages 1-13, October.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:4:p:43-:d:275407
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    References listed on IDEAS

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    5. Xanthi Pedeli & Anthony C. Davison & Konstantinos Fokianos, 2015. "Likelihood Estimation for the INAR( p ) Model by Saddlepoint Approximation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1229-1238, September.
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