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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.

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  • 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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    6. Nitin Kumar & Arvind Shrivastava & D. P. Singh & Purnendu Kumar, 2018. "Determinants of Financial Stress of Indian Banks," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 19(2), pages 210-228, September.

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