Predictive Accuracy of Impulse Responses Estimated Using Local Projections and Vector Autoregressions

Abstract

We examine the finite-sample accuracy of impulse responses obtained using local projections (LP) and vector autoregressive (VAR) models. In view of the fact that impulse responses are differences between multistep predictors, we propose to assess the relative performance of impulse-response estimators using tests for equal predictive accuracy. In our Monte Carlo experiments, LP-based and VAR-based estimators are found to be equally accurate in large samples under a mean-squared-error risk function. VAR-based estimators tend to have an advantage over LP-based estimators in small and moderately sized samples, particularly at long horizons.