February 2025 A log-linear model for non-stationary time series of counts
Anne Leucht, Michael H. Neumann
Author Affiliations +
Bernoulli 31(1): 709-730 (February 2025). DOI: 10.3150/24-BEJ1747

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.

Acknowledgments

This work was partly funded by Project “EcoDep” PSI-AAP2020 – 0000000013 and by “ProBe-Pro-Oberfranken” FP01054. The authors thank the editors and two anonymous referees for their valuable comments that led to a significant improvement of the paper.

Citation

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Anne Leucht. Michael H. Neumann. "A log-linear model for non-stationary time series of counts." Bernoulli 31 (1) 709 - 730, February 2025. https://doi.org/10.3150/24-BEJ1747

Information

Received: 1 June 2023; Published: February 2025
First available in Project Euclid: 30 October 2024

Digital Object Identifier: 10.3150/24-BEJ1747

Keywords: absolute regularity , count process , Log-linear model , Mixing , nonstationary process

Vol.31 • No. 1 • February 2025
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