Mixing properties of non-stationary INGARCH(1, 1) processes

Faculty/Professorship: Mathematics for Business and Economics  
Author(s): Doukhan, Paul; Leucht, Anne  ; Neumann, Michael H.
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 663–688
Source/Other editions: Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability. - (2021), Version: April 8, 2021, S. 663–688. - ISSN: 1573-9759
is version of: 10.3150/21-BEJ1362
Year of first publication: 2021
Language(s): English
Licence: German Act on Copyright 
URN: urn:nbn:de:bvb:473-irb-550041
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: 330 Economics  
RVK Classification: QH 170   
Peer Reviewed: Ja
International Distribution: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/55004
Release Date: 24. August 2022

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