TY  - CHAP
AB  - 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.
VL  - 40A
SN  - 978-1-78973-241-2, 978-1-78973-242-9/0731-9053
DO  - 10.1108/S0731-90532019000040A010
UR  - https://doi.org/10.1108/S0731-90532019000040A010
AU  - Chan Joshua C. C.
AU  - Jacobi Liana
AU  - Zhu Dan
PY  - 2019
Y1  - 2019/01/01
TI  - How Sensitive Are VAR Forecasts to Prior Hyperparameters? An Automated Sensitivity Analysis
T2  - Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
T3  - Advances in Econometrics
PB  - Emerald Publishing Limited
SP  - 229
EP  - 248
Y2  - 2024/09/24
ER  -