Bayesian forecasting with highly correlated predictors
AbstractThis 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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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 118 (2013)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/ecolet
Bayesian semiparametric selection; Dirichlet process prior; Correlated predictors; Clustered coefficients;
Other versions of this item:
- Dimitris Korobilis, 2012. "Bayesian Forecasting with Highly Correlated Predictors," Working Paper Series 67_12, The Rimini Centre for Economic Analysis.
- Dimitris Korobilis, 2012. "Bayesian forecasting with highly correlated predictors," Working Papers 2012_12, Business School - Economics, University of Glasgow.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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