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On Exponential‐Family INGARCH Models

Author

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  • Alan Huang
  • Thomas Fung
  • Kyle Macaskill
  • Rowan Aukes

Abstract

A range of integer‐valued generalised autoregressive conditional heteroscedastic (INGARCH) models have been proposed in the literature, including those based on conditional Poisson, negative binomial and Conway‐Maxwell‐Poisson distributions. This note considers a larger class of exponential‐family INGARCH models, showing that maximum empirical likelihood estimation over this semiparametric class of models can lead to consistent estimates as well as unbiased inferences on model parameters. The proposed framework is tested on two data analysis examples and a simulation study.

Suggested Citation

  • Alan Huang & Thomas Fung & Kyle Macaskill & Rowan Aukes, 2026. "On Exponential‐Family INGARCH Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 47(4), pages 912-918, July.
    • Handle: RePEc:bla:jtsera:v:47:y:2026:i:4:p:912-918
    DOI: 10.1111/jtsa.12831
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