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• W2131387906 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." @default.
• W2131387906 created "2016-06-24" @default.
• W2131387906 creator A5028320210 @default.
• W2131387906 creator A5050606907 @default.
• W2131387906 date "2010-04-01" @default.
• W2131387906 modified "2023-09-23" @default.
• W2131387906 title "Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML altimg=si113.gif display=inline overflow=scroll><mml:mi>t</mml:mi></mml:math> errors, and its application for forecasting" @default.
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• W2131387906 doi "https://doi.org/10.1016/j.ijforecast.2009.12.012" @default.
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