Bayesian Forecasting with Highly Correlated Predictors

16 Pages Posted: 19 Oct 2012

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: July 2012

Abstract

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Keywords: Bayesian semiparametric selection, Dirichlet process prior, correlated predictors, clustered coefficients

JEL Classification: C11, C14, C32, C52, C53

Suggested Citation

Korobilis, Dimitris, Bayesian Forecasting with Highly Correlated Predictors (July 2012). Available at SSRN: https://ssrn.com/abstract=2163870 or http://dx.doi.org/10.2139/ssrn.2163870

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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