Partial autocorrelation diagnostics for count time series
Publication date
2023-01-04
Document type
Forschungsartikel
Author
Organisational unit
ISSN
Series or journal
Entropy
Periodical volume
25
Periodical issue
1
Peer-reviewed
✅
Part of the university bibliography
✅
Keyword
Autoregressive model
Count time series
INAR bootstrap
Partial autocorrelation function
Yule–Walker equations
Abstract
In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule–Walker equations and thus provide the same PACF characterization as AR models, increased a lot. This motivates the use of the PACF test also for such count processes. By computing the sample PACF based on the raw data or the Pearson residuals, respectively, findings are usually evaluated based on well-known asymptotic results. However, the conditions for these asymptotics are generally not fulfilled for AR-type count processes, which deteriorates the performance of the PACF test in such cases. Thus, we present different implementations of the PACF test for AR-type count processes, which rely on several bootstrap schemes for count times series. We compare them in simulations with the asymptotic results, and we illustrate them with an application to a real-world data example.
Description
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Cite as
Weiß, C.H.; Aleksandrov, B.; Faymonville, M.; Jentsch, C. Partial Autocorrelation Diagnostics for Count Time Series. Entropy 2023, 25, 105. https://doi.org/10.3390/e25010105
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Published version
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