Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage
By Deborah Gefang. 2014
79 citations recorded by the source corpus.
We develop a novel Bayesian doubly adaptive elastic-net Lasso (DAELasso) approach for VAR shrinkage. DAELasso achieves variable selection and coefficient shrinkage in a data-based manner. It deals constructively with explanatory variables which tend to be highly collinear by encouraging the grouping effect. In addition, it also allows for different degrees of shrinkage 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. An empirical analysis shows that the forecast results produced by DAELasso and its variants are comparable to those from 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 than their complex and well-designed alternatives.
Subfields: Demography, Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e4c7d659e0205d565e46b5dcbf90fd16476674de
Citations in the corpus
- High‐dimensional macroeconomic forecasting and variable selection via penalized regression (2019, unknown): “A Bayesian VAR in high dimension is investigated by Banbura, Giannone and Reichlin (2010), Koop (2013) and Gefang (2014).”
- Identifying demand effects in a large network of product categories (2015, unknown): “…regularization techniques have been proposed for the VAR model in Litterman (1980) and are used in various applied fields such as macroeconomics (Gefang, 2014; Banbura et al., 2010), finance (Carriero et al., 2012) and marketing (Lenk and Orme, 2009; Horvath and Fok, 2013; Bandyopadhyay, 2009).”
- Adaptive Shrinkage in Bayesian Vector Autoregressive Models (2019, unknown): “Gefang (2014) proposed a Bayesian LASSO for VAR models, showing that forecast results tend to be similar to the ones obtained from standard Minnesota-type priors.”