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Title: Bayesian Doubly Adaptive Elastic-Net Lasso for VAR Shrinkage
Authors: Gefang, Deborah
First Published: 1-Oct-2013
Publisher: Elsevier for the International Institute of Forecasters
Citation: International Journal of Forecasting, 2014, 30 (1), pp. 1-11
Abstract: We develop a novel Bayesian doubly adaptive elastic-net Lasso (DAELasso) approach for VAR shrinkage. DAELasso achieves variable selection and coefficients shrinkage in a data based manner. It constructively deals with the explanatory variables that tend to be highly collinear by encouraging grouping effect. In addition, it allows for different degree of shrinkages for different coefficients. Rewriting the multivariate Laplace distribution as a scale mixture, we establish closed-form conditional posteriors that can be drawn from a Gibbs sampler. Empirical analysis shows that forecast results produced by DAELasso and its variants are comparable to that of other popular Bayesian methods, which provides further evidence that the forecast performances of large and medium sized Bayesian VARs are relatively robust to prior choices, and in practice simple Minnesota types of priors can be more attractive relative to their complex and well designed alternatives.
DOI Link: 10.1016/j.ijforecast.2013.04.004
ISSN: 0169-2070
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2013 International Institute of Forecasters. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website.
Description: NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting [Vol. 30, Issue 1, (2014)] DOI#10.1016/j.ijforecast.2013.04.004.
Appears in Collections:Published Articles, Dept. of Economics

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