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Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model

Author

Listed:
  • Jitendra Kumar

    (Central University of Rajasthan)

  • Ashok Kumar

    (MIT Art, Design & Technology University)

  • Varun Agiwal

    (Indian Institute of Public Health)

Abstract

In present scenario, handling covariate/explanatory variable with the model is one of most important factor to study with the models. The main advantages of covariate are it’s dependency on past observations. So, study variable is modelled after explaining both on own past and past and future observation of covariates. Present paper deals estimation of parameters of autoregressive model with multiple covariates under Bayesian approach. A simulation and empirical study is performed to check the applicability of the model and recorded the better results.

Suggested Citation

  • Jitendra Kumar & Ashok Kumar & Varun Agiwal, 2024. "Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model," Annals of Data Science, Springer, vol. 11(4), pages 1291-1301, August.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:4:d:10.1007_s40745-023-00468-2
    DOI: 10.1007/s40745-023-00468-2
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    References listed on IDEAS

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    1. Kai Yang & Dehui Wang, 2017. "Bayesian estimation for first-order autoregressive model with explanatory variables," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(22), pages 11214-11227, November.
    2. Yoosoon Chang & Robin C. Sickles & Wonho Song, 2017. "Bootstrapping unit root tests with covariates," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 136-155, March.
    3. Juhl, Ted & Xiao, Zhijie, 2003. "Power Functions And Envelopes For Unit Root Tests," Econometric Theory, Cambridge University Press, vol. 19(2), pages 240-253, April.
    4. Hansen, Bruce E., 1995. "Rethinking the Univariate Approach to Unit Root Testing: Using Covariates to Increase Power," Econometric Theory, Cambridge University Press, vol. 11(5), pages 1148-1171, October.
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