Misspecification-Robust Shrinkage and Selection for VAR Forecasts and IRFs

Abstract

Vector autoregressions (VARs) are vulnerable to dynamic misspecification, forcing researchers to navigate complex bias-variance trade-offs when forecasting or estimating impulse response functions (IRFs). We derive novel, task-specific selection criteria -- PC for forecasting and IRFC for IRF estimation -- based on asymptotically unbiased estimates of frequentist risk under local dynamic misspecification. These criteria give empirical researchers a fully data-driven and misspecification-aware workflow that simultaneously selects among candidate estimators, the degree of Bayesian shrinkage, and the lag length. IRFC is the first criterion allowing researchers to seamlessly choose between iterated-VAR and local-projection IRF estimators while jointly tuning shrinkage and lag length for the task at hand.