On exponential-family INGARCH models

Alan Huang; Thomas Fung; Kyle Macaskill; Rowan Aukes

doi:10.1111/jtsa.12831

On exponential-family INGARCH models

Alan Huang^*, Thomas Fung, Kyle Macaskill, Rowan Aukes

^*Corresponding author for this work

School of Mathematical and Physical Sciences

Research output: Contribution to journal › Comment/opinion › peer-review

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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.

Original language	English
Pages (from-to)	912-918
Number of pages	7
Journal	Journal of Time Series Analysis
Volume	47
Issue number	4
Early online date	15 Mar 2025
DOIs	https://doi.org/10.1111/jtsa.12831
Publication status	Published - Jul 2026

Bibliographical note

© 2025 The Author(s). Journal of Time Series Analysis published by John Wiley & Sons Ltd. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

discrete time-series
empirical likelihood
MSC-62
time-series of counts

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10.1111/jtsa.12831Licence: CC BY-NC-ND

Publisher version (open access)Final published version, 1.17 MBLicence: CC BY-NC-ND

Cite this

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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.",

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AU - Huang, Alan

AU - Fung, Thomas

AU - Macaskill, Kyle

AU - Aukes, Rowan

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N2 - 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.

AB - 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.

KW - discrete time-series

KW - empirical likelihood

KW - MSC-62

KW - time-series of counts

UR - https://www.scopus.com/pages/publications/105000364960

U2 - 10.1111/jtsa.12831

DO - 10.1111/jtsa.12831

M3 - Comment/opinion

AN - SCOPUS:105000364960

SN - 0143-9782

VL - 47

SP - 912

EP - 918

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

IS - 4

ER -

On exponential-family INGARCH models

Abstract

Bibliographical note

Keywords

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