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Conditional maximum likelihood estimation in negative binomial INGARCH processes with known number of successes when the true parameter is at the boundary of the parameter space

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  • Yunwei Cui
  • Xiaoyin Wang

Abstract

The paper establishes the asymptotic distribution of the conditional maximum likelihood estimator for integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) processes of conditional negative binomial distributions, with the number of successes in the definition of the negative binomial distribution being assumed to be known, when the true parameter is at the boundary of the parameter space. Based on the result, coefficient nullity tests are developed for model simplification. The proposed tests are investigated through a simulation study.

Suggested Citation

  • Yunwei Cui & Xiaoyin Wang, 2019. "Conditional maximum likelihood estimation in negative binomial INGARCH processes with known number of successes when the true parameter is at the boundary of the parameter space," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(13), pages 3388-3401, July.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:13:p:3388-3401
    DOI: 10.1080/03610926.2018.1476710
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    Cited by:

    1. Yunwei Cui & Rongning Wu & Qi Zheng, 2021. "Estimation of change‐point for a class of count time series models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1277-1313, December.

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