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Bayesian Doubly Adaptive Elastic-Net Lasso for VAR Shrinkage

journal contribution
posted on 2013-09-25, 15:24 authored by Deborah Gefang
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.

History

Citation

International Journal of Forecasting, 2014, 30 (1), pp. 1-11

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCE/Department of Economics

Version

  • AM (Accepted Manuscript)

Published in

International Journal of Forecasting

Publisher

Elsevier for the International Institute of Forecasters

issn

0169-2070

Copyright date

2013

Available date

2013-09-25

Publisher version

http://www.sciencedirect.com/science/article/pii/S0169207013000770

Notes

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.

Editors

Ruiz, E.

Language

en

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