Conferences at Department of Economics, University of Toronto, RCEF 2012: Cities, Open Economies, and Public Policy

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Bayesian Doubly Adaptive Elastic-Net Lasso For VAR Shrinkage

Deborah Gefang*

Last modified: 2012-07-13


We develop a novel Bayesian doubly adaptive elastic-net Lasso (DAELasso) approach for VAR shrinkage. DAELasso achieves data selection and coecients shrinkage in a data based manner. It constructively deals with the explanatory variables that tend to be highly collinear by encouraging grouping e ect. In addition, it allows for different degree of shrinkages for di erent coecients. Rewriting the multivariate Laplace distribution as a scale mixture, we establish closedform posteriors that can be drawn from a Gibbs sampler. We compare the forecasting performance of DAELasso to that of other popular Bayesian methods using US macro economic data. The results suggest that DAELasso is a useful complement to the available Baysian VAR shrinkage methods.

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