Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions

58 Pages Posted: 16 Dec 2016 Last revised: 2 Apr 2018

See all articles by Dimitris Korobilis

Dimitris Korobilis

affiliation not provided to SSRN

Davide Pettenuzzo

Brandeis University - Department of Economics

Date Written: March 28, 2018

Abstract

This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that allows fast approximate calculation of marginal parameter posterior distributions. We apply the algorithm to derive analytical expressions for independent VAR priors that admit a hierarchical representation and which would typically require computationally intensive posterior simulation methods. The bene fits of the new algorithm are explored using three quantitative exercises. First, a Monte Carlo experiment illustrates the accuracy and computational gains of the proposed estimation algorithm and priors. Second, a forecasting exercise involving VARs estimated on macroeconomic data demonstrates the ability of hierarchical shrinkage priors to find useful parsimonious representations. We also show how our approach can be used for structural analysis and that it can successfully replicate important features of news-driven business cycles predicted by a large-scale theoretical model.

Keywords: Bayesian VARs, Mixture Prior, Large Datasets, Macroeconomic Forecasting

JEL Classification: C11, C13, C32, C53

Suggested Citation

Korobilis, Dimitris and Pettenuzzo, Davide, Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions (March 28, 2018). Available at SSRN: https://ssrn.com/abstract=2886053 or http://dx.doi.org/10.2139/ssrn.2886053

Dimitris Korobilis

affiliation not provided to SSRN

Davide Pettenuzzo (Contact Author)

Brandeis University - Department of Economics ( email )

Waltham, MA 02454-9110
United States
781.736.2834 (Phone)
781.736.2269 (Fax)

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