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Bayesian Forecasting with Highly Correlated Predictors

Dimitris Korobilis

Working Paper series from Rimini Centre for Economic Analysis

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: 2012-11
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http://www.rcea.org/RePEc/pdf/wp67_12.pdf (application/pdf)

Related works:
Journal Article: Bayesian forecasting with highly correlated predictors (2013) Downloads
Working Paper: Bayesian forecasting with highly correlated predictors (2012) Downloads
Working Paper: Bayesian forecasting with highly correlated predictors (2012) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:67_12

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