On Exponential‐Family INGARCH Models
Year: 2025
Type: article
Abstract: 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.
Source: Journal of Time Series Analysis
DOI: 10.1111/jtsa.12831
Authors: Alan Huang, Thomas Fung, Kyle Macaskill, Rowan Aukes
Institutions: The University of Queensland, Macquarie University
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,510
OpenAlex ID: W4408482026