Title: How sensitive are VAR forecasts to prior hyperparameters: An automated sensitivity analysis
Authors: Liana Jacobi - University Melbourne (Australia)
Joshua Chan - Australian National University (Australia)
Dan Zhu - Monash University (Australia) [presenting]
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 a new automatic differentiation approach for MCMC output 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.