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

25 Pages Posted: 3 Jun 2018

See all articles by Joshua Chan

Joshua Chan

University of Technology Sydney (UTS)

Liana Jacobi

University of Melbourne - Faculty of Business and Economics; IZA Institute of Labor Economics

Dan Zhu

Monash University - Department of Econometrics & Business Statistics

Multiple version iconThere are 2 versions of this paper

Date Written: May 29, 2018

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

JEL Classification: C11, C53, E37

Suggested Citation

Chan, Joshua and Jacobi, Liana and Zhu, Dan, How Sensitive are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis (May 29, 2018). CAMA Working Paper No. 25/2018. Available at SSRN: https://ssrn.com/abstract=3187046 or http://dx.doi.org/10.2139/ssrn.3187046

Joshua Chan (Contact Author)

University of Technology Sydney (UTS) ( email )

15 Broadway, Ultimo
PO Box 123
Sydney, NSW 2007
Australia

Liana Jacobi

University of Melbourne - Faculty of Business and Economics ( email )

Victoria, 3010
Australia

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Dan Zhu

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

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