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A pth-order random coefficients mixed binomial autoregressive process with explanatory variables

Author

Listed:
  • Han Li

    (Changchun University
    Changchun University of Technology)

  • Zijian Liu

    (Changchun University of Technology)

  • Kai Yang

    (Changchun University of Technology)

  • Xiaogang Dong

    (Changchun University of Technology)

  • Wenshan Wang

    (Changchun University of Technology)

Abstract

To capture the higher-order autocorrelation structure for finite-range integer-valued time series of counts, and to consider the driving effect of covariates on the underlying process, this paper introduces a pth-order random coefficients mixed binomial autoregressive process with explanatory variables. The basic probabilistic and statistical properties of the model are discussed. Conditional least squares and conditional maximum likelihood estimators, as well as their asymptotic properties of the estimators are obtained. Moreover, the existence test of explanatory variables are well addressed using a Wald-type test. Forecasting problem is also considered. Finally, some numerical results of the estimators and a real data example are presented to show the performance of the proposed model.

Suggested Citation

  • Han Li & Zijian Liu & Kai Yang & Xiaogang Dong & Wenshan Wang, 2024. "A pth-order random coefficients mixed binomial autoregressive process with explanatory variables," Computational Statistics, Springer, vol. 39(5), pages 2581-2604, July.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:5:d:10.1007_s00180-023-01396-8
    DOI: 10.1007/s00180-023-01396-8
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    References listed on IDEAS

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    1. Kai Yang & Dehui Wang & Boting Jia & Han Li, 2018. "An integer-valued threshold autoregressive process based on negative binomial thinning," Statistical Papers, Springer, vol. 59(3), pages 1131-1160, September.
    2. Scotto, Manuel G. & Weiß, Christian H. & Silva, Maria Eduarda & Pereira, Isabel, 2014. "Bivariate binomial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 233-251.
    3. Freeland, R. K. & McCabe, B. P. M., 2004. "Forecasting discrete valued low count time series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 427-434.
    4. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    5. Yang, Kai & Yu, Xinyang & Zhang, Qingqing & Dong, Xiaogang, 2022. "On MCMC sampling in self-exciting integer-valued threshold time series models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    6. Chao Wang & Heng Liu & Jian-Feng Yao & Richard A. Davis & Wai Keung Li, 2014. "Self-Excited Threshold Poisson Autoregression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 777-787, June.
    7. Kai Yang & Han Li & Dehui Wang & Chenhui Zhang, 2021. "Random coefficients integer-valued threshold autoregressive processes driven by logistic regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 533-557, December.
    8. Kurt Brännäs & Jonas Nordström, 2006. "Tourist Accommodation Effects of Festivals," Tourism Economics, , vol. 12(2), pages 291-302, June.
    9. Rong Zhu & Harry Joe, 2006. "Modelling Count Data Time Series with Markov Processes Based on Binomial Thinning," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 725-738, September.
    10. Christian H. Weiß & Philip K. Pollett, 2014. "Binomial Autoregressive Processes With Density-Dependent Thinning," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(2), pages 115-132, March.
    11. Cathy W. S. Chen & Khemmanant Khamthong & Sangyeol Lee, 2019. "Markov switching integer‐valued generalized auto‐regressive conditional heteroscedastic models for dengue counts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(4), pages 963-983, August.
    12. Xanthi Pedeli & Anthony C. Davison & Konstantinos Fokianos, 2015. "Likelihood Estimation for the INAR( p ) Model by Saddlepoint Approximation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1229-1238, September.
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