Bayesian Forecasting with Highly Correlated Predictors

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.

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Year of publication:	2012-11
Authors:	Korobilis, Dimitris
Institutions:	Rimini Centre for Economic Analysis (RCEA)
Subject:	Bayesian semiparametric selection \| Dirichlet process prior \| correlated predictors \| clustered coefficients

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Extent:	application/pdf
Series:	Working Paper Series.
Type of publication:	Book / Working Paper
Classification:	C11 - Bayesian Analysis ; C14 - Semiparametric and Nonparametric Methods ; C32 - Time-Series Models ; C52 - Model Evaluation and Testing ; C53 - Forecasting and Other Model Applications
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010614521