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Bayesian Analysis of Student t Linear Regression with Unknown Change-Point and Application to Stock Data Analysis

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  • Jin-Guan Lin
  • Ji Chen
  • Yong Li

Abstract

This article devotes to studying the variance change-points problem in student t linear regression models. By exploiting the equivalence of the student t distribution and an appropriate scale mixture of normal distributions, a Bayesian approach combined with Gibbs sampling is developed to detect the single and multiple change points. Some simulation studies are performed to display the process of the detection and investigate the effects of the developed approach. Finally, for illustration, the Dow Jones index closed data of U.S. market are analyzed and three variance change-points are detected. Copyright Springer Science+Business Media, LLC. 2012

Suggested Citation

  • Jin-Guan Lin & Ji Chen & Yong Li, 2012. "Bayesian Analysis of Student t Linear Regression with Unknown Change-Point and Application to Stock Data Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 203-217, October.
    • Handle: RePEc:kap:compec:v:40:y:2012:i:3:p:203-217
    DOI: 10.1007/s10614-011-9305-8
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    References listed on IDEAS

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    1. Jin-Guan Lin & Li-Xing Zhu & Feng-Chang Xie, 2009. "Heteroscedasticity diagnostics for t linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(1), pages 59-77, June.
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    Cited by:

    1. Shuaimin Kang & Guangying Liu & Howard Qi & Min Wang, 2018. "Bayesian Variance Changepoint Detection in Linear Models with Symmetric Heavy-Tailed Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 459-477, August.
    2. Kang-Ping Lu & Shao-Tung Chang, 2022. "Robust Switching Regressions Using the Laplace Distribution," Mathematics, MDPI, vol. 10(24), pages 1-24, December.
    3. Kang-Ping Lu & Shao-Tung Chang, 2021. "Robust Algorithms for Change-Point Regressions Using the t -Distribution," Mathematics, MDPI, vol. 9(19), pages 1-28, September.

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