Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Non-Fundamentalness

Abstract

Non-fundamentalness arises when observables do not contain enough information to recover the vector of structural shocks. Using Granger causality tests, the literature suggested that many small scale VAR models are non-fundamental and thus not useful for business cycle analysis. We show that causality tests are problematic when VAR variables are cross sectionally aggregated or proxy for non-observables. We provide an alternative testing procedure, illustrate its properties with a Monte Carlo exercise, and reexamine the properties of two prototypical VAR models.