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Mixing properties of non-stationary INGARCH(1, 1) processes
Doukhan, Paul; Leucht, Anne; Neumann, Michael H. (2022): Mixing properties of non-stationary INGARCH(1, 1) processes, in: Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, Aarhus, Jg. 28, Nr. 1, S. 663–688, doi: 10.3150/21-BEJ1362.
Faculty/Chair:
Author:
Title of the Journal:
Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability
ISSN:
1573-9759
Publisher Information:
Year of publication:
2022
Volume:
28
Issue:
1
Pages:
Language:
English
DOI:
Abstract:
We derive mixing properties for a broad class of Poisson count time series satisfying a certain contraction condition. Using specific coupling techniques, we prove absolute regularity at a geometric rate not only for stationary Poisson-GARCH processes but also for models with an explosive trend. We provide easily verifiable sufficient conditions for absolute regularity for a variety of models including classical (log-)linear models. Finally, we illustrate the practical use of our results for hypothesis testing.
GND Keywords: ; ; ; ;
Poisson-Prozess
GARCH-Prozess
Lineares Modell
Regelmäßigkeit
Zeitreihe
Keywords: ;
Absolute regularity
INGARCH
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Type:
Article
Activation date:
November 16, 2021
Versioning
Question on publication
Permalink
https://fis.uni-bamberg.de/handle/uniba/52082