How Sensitive Are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis

Joshua Chan, Liana Jacobi and Dan Zhu

A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, 2019, vol. 40A, pp 229-248 from Emerald Group Publishing Limited

Abstract: Vector autoregressions (VAR) 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; model comparison; sensitivity analysis; prior robustness; C11; C53; E37 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Working Paper: How sensitive are VAR forecasts to prior hyperparameters? An automated sensitivity analysis (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532019000040a010

DOI: 10.1108/S0731-90532019000040A010

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