Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior

Lendie Follett and Cindy Yu

Econometrics and Statistics, 2019, vol. 11, issue C, 130-144

Abstract: In the context of a vector autoregression (VAR) model, or any multivariate regression model, the number of relevant predictors may be small relative to the information set that is available. It is well known that forecasts based on (un-penalized) least squares estimates can overfit the data and lead to poor predictions. Since the Minnesota prior was proposed, there have been many methods developed aiming at improving prediction performance. The horseshoe prior is proposed in the context of a Bayesian VAR. The horseshoe prior is a unique shrinkage prior scheme in that it shrinks irrelevant signals rigorously to 0 while allowing large signals to remain large and practically unshrunk. In an empirical study, it is shown that the horseshoe prior competes favorably with shrinkage schemes commonly used in Bayesian VAR models as well as with a prior that imposes true sparsity in the coefficient vector. Additionally, the use of particle Gibbs with backwards simulation is proposed for the estimation of the time-varying volatility parameters. A detailed description of relevant MCMC methods is provided in the supplementary material.

Keywords: MCMC; Shrinkage; Sparsity; Bayesian VAR; Macroeconomic forecasting (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2452306219300036
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:11:y:2019:i:c:p:130-144

DOI: 10.1016/j.ecosta.2018.12.004

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().