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Conditional maximum likelihood estimation for a class of observation-driven time series models for count data

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  • Cui, Yunwei
  • Zheng, Qi

Abstract

This paper investigates the statistical inference for a class of observation-driven time series models of count data based on the conditional maximum likelihood estimator (CMLE), where the conditional distribution of the observed count given a state process is from the one-parameter exponential family. Under certain regularity conditions, the strong consistency and asymptotic normality of the CMLE of the misspecified likelihood function are established.

Suggested Citation

  • Cui, Yunwei & Zheng, Qi, 2017. "Conditional maximum likelihood estimation for a class of observation-driven time series models for count data," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 193-201.
    • Handle: RePEc:eee:stapro:v:123:y:2017:i:c:p:193-201
    DOI: 10.1016/j.spl.2016.11.002
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    References listed on IDEAS

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    Cited by:

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    4. Jiménez-Gamero, M.D. & Alba-Fernández, M.V., 2019. "Testing for the Poisson–Tweedie distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 164(C), pages 146-162.
    5. 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.

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