Goodness-of-fit tests for Poisson count time series based on the Stein–Chen identity
Publication date
2021-06-27
Document type
Forschungsartikel
Author
Organisational unit
Publisher
Wiley-Blackwell
Series or journal
Statistica Neerlandica
ISSN
Periodical volume
76
Periodical issue
1
First page
35
Last page
64
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Bivariate Poisson distribution
Count time series
Diagnostic tests
Stein–Chen identity
Bootstrap
INARMA models
Abstract
To test the null hypothesis of a Poisson marginal distribution, test statistics based on the Stein–Chen identity are proposed. For a wide class of Poisson count time series, the asymptotic distribution of different types of Stein–Chen statistics is derived, also if multiple statistics are jointly applied. The performance of the tests is analyzed with simulations, as well as the question which Stein–Chen functions should be used for which alternative. Illustrative data examples are presented, and possible extensions of the novel Stein–Chen approach are discussed as well.
Description
This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Version
Published version
Access right on openHSU
Metadata only access