Approximately linear INGARCH models for spatio-temporal counts
Publication date
2023-03-16
Document type
Forschungsartikel
Author
Organisational unit
Publisher
Oxford University Press
Series or journal
Journal of the Royal Statistical Society. Series C, Applied statistics
ISSN
Periodical volume
72
Periodical issue
2
First page
476
Last page
497
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Gaussian copula
Soft clipping
Softplus
Spatial error model
Spatio-temporal counts
Abstract
Existing integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models for spatio-temporal counts do not allow for negative parameter and autocorrelation values. Using approximately linear INGARCH models, the unified and flexible spatio-temporal (B)INGARCH framework for modelling unbounded (bounded) counts is proposed. These models combine negative dependencies with kinds of a long memory. They are easily adapted to special marginal features or cross-dependencies: When modelling precipitation data (counts of rainy hours), we account for zero-inflation, while for cloud-coverage data (counts of okta), we deal with missing data and additional cross-correlation. A copula related to the spatial error model shows an appealing performance.
Description
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/).
Cite as
Malte Jahn, Christian H Weiß, Hee-Young Kim, Approximately linear INGARCH models for spatio-temporal counts, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 72, Issue 2, May 2023, Pages 476–497, https://doi.org/10.1093/jrsssc/qlad018
Version
Published version
Access right on openHSU
Metadata only access