Abstract
Based on the idea of Kuo and Mallick (1994) we extend the variable selection procedure by Gibbs sampling in Bayesian AR and Vector AR (B-VAR) processes. The method is also extended to ARCH models, switching regression models, and variance selection in SUR models. Selected simulated and real examples will demonstrate the approaches which avoid the combinatorial explosions of traditional methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
L. Kuo and B. Mallick (1994) Variable Selection for Regression Models, mimeo, Univ. of Connecticut.
Gelfand A.E., and A.F.M. Smith (1990) Sampling based approaches to calculating marginal densities, JASA 85, 398–409.
George and McCulloch (1993) Variable selection via Gibbs sampling, J. of the American Statistical Association 88, 881–889.
Littermann R.B. (1986) A statistical approach to economic forecasting, J. of Business and Ec. Stat. 4, 1–24.
Polasek W. and Kozumi H. (1995) The VAR-VARCH model: A Bayesian approach, WWZ, University of Basel.
Polasek W. and S. Jin (1994) Gibbs sampling in AR models with random walk prior, mimeo, University of Basel.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Physica-Verlag Heidelberg
About this chapter
Cite this chapter
Polasek, W., Jin, S. (1996). Variable Selection and Prediction in B-VAR Models. In: Bol, G., Nakhaeizadeh, G., Vollmer, KH. (eds) Finanzmarktanalyse und -prognose mit innovativen quantitativen Verfahren. Wirtschaftswissenschaftliche Beiträge, vol 125. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-61489-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-61489-7_10
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0925-1
Online ISBN: 978-3-642-61489-7
eBook Packages: Springer Book Archive