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Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting

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  • Zellner, Arnold
  • Ando, Tomohiro

Abstract

A description of computationally efficient methods for the Bayesian analysis of Student-t seemingly unrelated regression (SUR) models with unknown degrees of freedom is given. The method combines a direct Monte Carlo (DMC) approach with an importance sampling procedure to calculate Bayesian estimation and prediction results using a diffuse prior. This approach is employed to compute Bayesian posterior densities for the parameters, as well as predictive densities for future values of variables and the associated moments, intervals and other quantities that are useful to both forecasters and others. The results obtained using our approach are compared to those yielded by the use of DMC for a standard normal SUR model.

Suggested Citation

  • Zellner, Arnold & Ando, Tomohiro, 2010. "Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting," International Journal of Forecasting, Elsevier, vol. 26(2), pages 413-434, April.
  • Handle: RePEc:eee:intfor:v:26:y::i:2:p:413-434
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    References listed on IDEAS

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    1. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    2. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, vol. 5(05), pages 673-700, November.
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    5. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
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    Citations

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    Cited by:

    1. Arnold Zellner & Tomohiro Ando & Nalan Baştük & Lennart Hoogerheide & Herman K. van Dijk, 2014. "Bayesian Analysis of Instrumental Variable Models: Acceptance-Rejection within Direct Monte Carlo," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 3-35, June.
    2. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    3. Nomen Nescio, 2013. "Nomen Nescio," Tinbergen Institute Discussion Papers 12-095 not issued, Tinbergen Institute.

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