EconPapers    
Economics at your fingertips  
 

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Florian Huber and Martin Feldkircher

Journal of Business & Economic Statistics, 2019, vol. 37, issue 1, 27-39

Abstract: Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this article, we apply the Normal-Gamma shrinkage prior to the VAR with stochastic volatility case and derive its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariance parameters of the VAR along with Gamma priors on a set of local and global prior scaling parameters. In a second step, we modify this prior setup by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. Two simulation exercises show that the proposed framework yields more precise estimates of model parameters and impulse response functions. In addition, a forecasting exercise applied to U.S. data shows that this prior performs well relative to other commonly used specifications in terms of point and density predictions. Finally, performing structural inference suggests that responses to monetary policy shocks appear to be reasonable.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (85)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2016.1256217 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Adaptive shrinkage in Bayesian vector autoregressive models (2016) Downloads
Working Paper: Adaptive Shrinkage in Bayesian Vector Autoregressive Models (2016) Downloads
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:taf:jnlbes:v:37:y:2019:i:1:p:27-39

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2016.1256217

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-27
Handle: RePEc:taf:jnlbes:v:37:y:2019:i:1:p:27-39