Adaptive hierarchical priors for high-dimensional vector autoregressions
Dimitris Korobilis and
Davide Pettenuzzo ()
Journal of Econometrics, 2019, vol. 212, issue 1, 241-271
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.
Keywords: Bayesian VARs; Mixture prior; Large datasets; Macroeconomic forecasting (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 C53 (search for similar items in EconPapers)
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Working Paper: Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions (2018)
Working Paper: Adaptive Hierarchical Priors for High-Dimensional Vector Autoregessions (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:1:p:241-271
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