Hierarchical shrinkage priors for dynamic regressions with many predictors

Dimitris Korobilis

No 2011021, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using this single hierarchical Bayes formulation. Using 129 U.S. macroeconomic quarterly variables for the period 1959 – 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results show that for particular data series hierarchical shrinkage dominates factor model forecasts, and hence is a valuable addition to existing methods for handling large dimensional data.

Keywords: forecasting; shrinkage; factor model; variable selection; Bayesian LASSO (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 C53 C63 E37 (search for similar items in EconPapers)
Date: 2011-05-01
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Hierarchical shrinkage priors for dynamic regressions with many predictors (2013)
Working Paper: Hierarchical shrinkage priors for dynamic regressions with many predictors (2011)
Working Paper: Hierarchical Shrinkage Priors for Dynamic Regressions with Many Predictors (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2011021

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