Korobilis, Dimitris (2011): Hierarchical shrinkage priors for dynamic regressions with many predictors.

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Abstract
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical NormalGamma 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 it becomes a valuable addition to existing methods for handling large dimensional data.
Item Type:  MPRA Paper 

Original Title:  Hierarchical shrinkage priors for dynamic regressions with many predictors 
Language:  English 
Keywords:  Forecasting; shrinkage; factor model; variable selection; Bayesian LASSO 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes E  Macroeconomics and Monetary Economics > E3  Prices, Business Fluctuations, and Cycles > E37  Forecasting and Simulation: Models and Applications C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  30380 
Depositing User:  Dimitris Korobilis 
Date Deposited:  25 Apr 2011 02:43 
Last Modified:  02 Oct 2019 17:29 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/30380 