Goodness-of-fit testing in bivariate count time series based on a bivariate dispersion index
Publication date
2024-09-17
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Springer
Series or journal
AStA Advances in Statistical Analysis
ISSN
Periodical volume
109
Periodical issue
2
First page
241
Last page
279
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Asymptotic distribution
Bivariate dispersion index
Bivariate INAR(1) model
Bivariate Poisson distribution
Count time series
Abstract
A common choice for the marginal distribution of a bivariate count time series is the bivariate Poisson distribution. In practice, however, when the count data exhibit zero inflation, overdispersion or non-stationarity features, such that a marginal bivariate Poisson distribution is not suitable. To test the discrepancy between the actual count data and the bivariate Poisson distribution, we propose a new goodness-of-fit test based on a bivariate dispersion index. The asymptotic distribution of the test statistic under the null hypothesis of a first-order bivariate integer-valued autoregressive model with marginal bivariate Poisson distribution is derived, and the finite-sample performance of the goodness-of-fit test is analyzed by simulations. A real-data example illustrate the application and usefulness of the test in practice.
Description
This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Version
Published version
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