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 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
Authors:	Korobilis, Dimitris
Publisher:	[2012]: [S.l.] : SSRN
Subject:	Prognoseverfahren \| Forecasting model \| Bayes-Statistik \| Bayesian inference \| Korrelation \| Correlation \| Theorie \| Theory \| Frühindikator \| Leading indicator

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Extent:	1 Online-Ressource (16 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 2012 erstellt
Other identifiers:	10.2139/ssrn.2163870 [DOI]
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:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013099177