Bayesian forecasting with highly correlated predictors

Dimitris Korobilis

Working Papers from Business School - Economics, University of Glasgow

Abstract: This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by 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 (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C52 C53 (search for similar items in EconPapers)
Date: 2012-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://www.gla.ac.uk/media/media_241548_en.pdf (application/pdf)

Related works:
Journal Article: Bayesian forecasting with highly correlated predictors (2013)
Working Paper: Bayesian forecasting with highly correlated predictors (2012)
Working Paper: Bayesian Forecasting with Highly Correlated Predictors (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2012_12

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