Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting
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.Download Info
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Bibliographic Info
Article provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 26 (2010)
Issue (Month): 2 (April)
Pages: 413-434
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Web page: http://www.elsevier.com/locate/ijforecast
Related research
Keywords: Direct Monte Carlo Heavy tail behavior Importance sampling Markov chain Monte Carlo;References
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Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- repec:dgr:uvatin:20120095 is not listed on IDEAS
- 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.
- Arnold Zellner (posthumously) & Tomohiro Ando & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2012. "Bayesian Analysis of Instrumental Variable Models: Acceptance-Rejection within Direct Monte Carlo," Tinbergen Institute Discussion Papers 12-098/III, Tinbergen Institute.
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