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

Joshua C. C. Chan, Liana Jacobi, Dan Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review


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.
Original languageEnglish
Title of host publicationTopics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling
Subtitle of host publicationPart A
EditorsIvan Jeliazkov, Justin L. Tobias
Place of PublicationBingley UK
PublisherEmerald Group Publishing Limited
Number of pages20
ISBN (Electronic)9781789732412, 9781789732436
ISBN (Print)9781789732429
Publication statusPublished - 2019

Publication series

NameAdvances in Econometrics
ISSN (Electronic)0731-9053


  • Vector autoregression
  • Automatic differentiation
  • Interval forecasts
  • Model comparison
  • Sensitivity analysis
  • Prior robustness

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