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Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

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  • Nachatchapong Kaewsompong
  • Paravee Maneejuk
  • Woraphon Yamaka

Abstract

We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure, thus, we apply the multivariate exchangeable copula function to relax this assumption. As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. The real data analysis is also provided, and the empirical comparison indicates our proposed model outperforms the conventional models in all considered quantile levels.

Suggested Citation

  • Nachatchapong Kaewsompong & Paravee Maneejuk & Woraphon Yamaka, 2020. "Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models," Complexity, Hindawi, vol. 2020, pages 1-15, July.
    • Handle: RePEc:hin:complx:6746303
    DOI: 10.1155/2020/6746303
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    Cited by:

    1. Woraphon Yamaka & Rangan Gupta & Sukrit Thongkairat & Paravee Maneejuk, 2023. "Structural and predictive analyses with a mixed copula‐based vector autoregression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 223-239, March.

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