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Robust estimation for general integer-valued time series models

Author

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  • Byungsoo Kim

    (Yeungnam University)

  • Sangyeol Lee

    (Seoul National University)

Abstract

In this study, we consider a robust estimation method for general integer-valued time series models whose conditional distribution belongs to the one-parameter exponential family. As a robust estimator, we employ the minimum density power divergence estimator, and we demonstrate this is strongly consistent and asymptotically normal under certain regularity conditions. A simulation study is carried out to evaluate the performance of the proposed estimator. A real data analysis using the return times of extreme events of the Goldman Sachs Group stock is also provided as an illustration.

Suggested Citation

  • Byungsoo Kim & Sangyeol Lee, 2020. "Robust estimation for general integer-valued time series models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(6), pages 1371-1396, December.
    • Handle: RePEc:spr:aistmt:v:72:y:2020:i:6:d:10.1007_s10463-019-00728-0
    DOI: 10.1007/s10463-019-00728-0
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    Cited by:

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    2. Lee, Sangyeol & Kim, Dongwon & Kim, Byungsoo, 2023. "Modeling and inference for multivariate time series of counts based on the INGARCH scheme," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

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