Abstract
Parameter change test has been an important issue in time series analysis. The problem has also been actively explored in the field of integer-valued time series, but the testing in the presence of outliers has not yet been extensively investigated. This study considers the problem of testing for parameter change in Poisson autoregressive models particularly when observations are contaminated by outliers. To lessen the impact of outliers on testing procedure, we propose a test based on the density power divergence, which is introduced by Basu et al. (Biometrika 85:549–559, 1998), and derive its limiting null distribution. Monte Carlo simulation results demonstrate validity and strong robustness of the proposed test.
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Acknowledgements
We would like to thank the referees for carefully examining the paper and providing valuable comments that improved its quality. This work was supported by the research grant of Jeju National University in 2019.
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Kang, J., Song, J. A robust approach for testing parameter change in Poisson autoregressive models. J. Korean Stat. Soc. 49, 1285–1302 (2020). https://doi.org/10.1007/s42952-020-00056-7
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DOI: https://doi.org/10.1007/s42952-020-00056-7