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Hierarchical shrinkage priors for dynamic regressions with many predictors

Korobilis, D. (2013) Hierarchical shrinkage priors for dynamic regressions with many predictors. International Journal of Forecasting, 29 (1). ISSN 0169-2070 (doi:10.1016/j.ijforecast.2012.05.006)

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Publisher's URL: http://dx.doi.org/10.1016/j.ijforecast.2012.05.006

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 U.S. macroeconomic quarterly variables for the period 1959-2010 I extensively evaluate the forecasting properties of Bayesian shrinkage in macroeconomic forecasting with many predictors. Results show that for particular data series hierarchical shrinkage dominates factor model forecasts and, hence, it serves as a valuable addition to existing methods for handling large dimensional data.

Item Type:Article
Keywords:Forecasting; shrinkage; factor model; variable selection; Bayesian lasso
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Korobilis, Dr Dimitris
Authors: Korobilis, D.
Subjects:H Social Sciences > HA Statistics
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:International Journal of Forecasting
Publisher:Elsevier
ISSN:0169-2070

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