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Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions

Korobilis, Dimitris and Pettenuzzo, Davide (2019) 'Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions.' Journal of Econometrics. ISSN 0304-4076

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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 benefits 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.

Item Type: Article
Uncontrolled Keywords: Bayesian VARs, Mixture prior, Large datasets, Macroeconomic forecasting
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 13 Sep 2018 13:06
Last Modified: 07 Aug 2019 21:15
URI: http://repository.essex.ac.uk/id/eprint/22994

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