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Hierarchical shrinkage priors for dynamic regressions with many predictors

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  • Korobilis, Dimitris

Abstract

This paper examines the properties of Bayes shrinkage estimators for dynamic regressions that are based on hierarchical versions of the typical normal prior. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using a single hierarchical Bayes formulation. Using 129 US macroeconomic quarterly variables for the period 1959–2010, I extensively evaluate the forecasting properties of Bayesian shrinkage in macroeconomic forecasting with many predictors. The results show that, for particular data series, hierarchical shrinkage dominates factor model forecasts, and hence serves as a valuable addition to the existing methods for handling large dimensional data.

Suggested Citation

  • Korobilis, Dimitris, 2013. "Hierarchical shrinkage priors for dynamic regressions with many predictors," International Journal of Forecasting, Elsevier, vol. 29(1), pages 43-59.
  • Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:43-59
    DOI: 10.1016/j.ijforecast.2012.05.006
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    More about this item

    Keywords

    Forecasting; Shrinkage; Factor model; Variable selection; Bayesian lasso;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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