How sensitive are VAR forecasts to prior hyperparameters? An automated sensitivity analysis

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: Vector autoregressions combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyperparameters. But the nature of this sensitivity is seldom investigated. We develop a general method based on Automatic Differentiation to systematically compute the sensitivities of forecasts—both points and intervals—with respect to any prior hyperparameters. In a forecasting exercise using US data, we find that forecasts are relatively sensitive to the strength of shrinkage for the VAR coefficients, but they are not much affected by the prior mean of the error covariance matrix or the strength of shrinkage for the intercepts.

Keywords: vector autoregression; automatic differentiation; interval forecasts (search for similar items in EconPapers)
JEL-codes: C11 C53 E37 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2018-05
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://cama.crawford.anu.edu.au/sites/default/fil ... _chan_jacobi_zhu.pdf (application/pdf)

Related works:
Chapter: How Sensitive Are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2018-25

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