Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation

Joshua Chan, Liana Jacobi and Dan Zhu

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Large Bayesian VARs with the natural conjugate prior are now routinely used for forecasting and structural analysis. It has been shown that selecting the prior hyperparameters in a data-driven manner can often substantially improve forecast performance. We propose a computationally efficient method to obtain the optimal hyperparameters based on Automatic Differentiation, which is an efficient way to compute derivatives. Using a large US dataset, we show that using the optimal hyperparameter values leads to substantially better forecast performance. Moreover, the proposed method is much faster than the conventional grid-search approach, and is applicable in high-dimensional optimization problems. The new method thus provides a practical and systematic way to develop better shrinkage priors for forecasting in a data-rich environment.

Keywords: automatic differentiation; vector autoregression; optimal hyperparameters; forecasts; marginal likelihood (search for similar items in EconPapers)
JEL-codes: C11 C53 E37 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2019-06
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2019-46

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