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A log-linear model for non-stationary time series of counts
Leucht, Anne; Neumann, Michael H. (2025): A log-linear model for non-stationary time series of counts, in: Bernoulli, Aarhus, Jg. 31, Nr. 1, S. 709–730, doi: 10.3150/24-bej1747.
Faculty/Chair:
Author:
Title of the Journal:
Bernoulli
ISSN:
1573-9759
Corporate Body:
Bernoulli Society for Mathematical Statistics and Probability
Publisher Information:
Year of publication:
2025
Volume:
31
Issue:
1
Pages:
Language:
English
Remark:
Preprint veröffentlicht bei arXiv unter: https://doi.org/10.48550/arXiv.2307.01315
DOI:
Abstract:
We propose a new model for non-stationary integer-valued time series which is particularly suitable for data with a strong trend. In contrast to popular Poisson-INGARCH models, but in line with classical GARCH models, we propose to pick the conditional distributions from nearly scale invariant families where the mean absolute value and the standard deviation are of the same order of magnitude. As an important prerequisite for applications in statistics, we prove absolute regularity of the count process with exponentially decaying coefficients.
Keywords: ;  ;  ;  ; 
Absolute regularity
count process
log-linear model
mixing
nonstationary process
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Type:
Article
Activation date:
November 22, 2024
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Question on publication
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https://fis.uni-bamberg.de/handle/uniba/104951