Marginal analysis of count time series in the presence of missing observations
Publication date
2024
Document type
Forschungsartikel
Author
Organisational unit
ISSN
Series or journal
TEST
Periodical volume
33
First page
1105
Last page
1128
Peer-reviewed
✅
Part of the university bibliography
✅
Keyword
Amplitude modulation
Dispersion index
Skewness index
Missing data
Poisson autoregressive model
Binomial autoregressive model
Abstract
Time series in real-world applications often have missing observations, making typical analytical methods unsuitable. One method for dealing with missing data is the concept of amplitude modulation. While this principle works with any data, here, missing data for unbounded and bounded count time series are investigated, where tailor-made dispersion and skewness statistics are used for model diagnostics. General closed-form asymptotic formulas are derived for such statistics with only weak assumptions on the underlying process. Moreover, closed-form formulas are derived for the popular special cases of Poisson and binomial autoregressive processes, always under the assumption that missingness occurs. The finite-sample performances of the considered asymptotic approximations are analyzed with simulations. The practical application of the corresponding dispersion and skewness tests under missing data is demonstrated with three real data examples.
Description
This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Version
Published version
Access right on openHSU
Metadata only access
Open Access Funding
Springer Nature (DEAL)