Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Feldkircher, Martin ORCID: https://orcid.org/0000-0002-5511-9215 and Huber, Florian ORCID: https://orcid.org/0000-0002-2896-7921 (2016) Adaptive Shrinkage in Bayesian Vector Autoregressive Models. Department of Economics Working Paper Series, 221. WU Vienna University of Economics and Business, Vienna.

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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 paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. A simulation exercise shows that the proposed framework yields more precise estimates of the model parameters and impulse response functions. In addition, a forecasting exercise applied to US data shows that the proposed prior outperforms other specifications in terms of point and density predictions. (authors' abstract)

Item Type:	Paper
Keywords:	Normal-Gamma prior / density predictions / hierarchical modeling
Classification Codes:	JEL C11, C30, C53, E52
Divisions:	Departments > Volkswirtschaft
Depositing User:	Claudia Tering-Raunig
Date Deposited:	22 Mar 2016 12:05
Last Modified:	07 May 2021 16:30
URI:	https://epub.wu.ac.at/id/eprint/4933

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