Innovation meet Narratives

Abstract: In macro, instances of clear and indisputable exogenous variation are rare and researchers often face a difficult trade-off between credibility and efficiency. In this work, we introduce a new method —innovation-powered IV—, which allows to reduce the confidence intervals of a credible but low power identification scheme (e.g., a narrative instrument) by leveraging the high power of a possibly mis-specified parametric identification assumption (e.g., a short run restriction). The method delivers large reductions in confidence intervals for the causal effects of monetary and fiscal policy, with gains of around 40 percent compared to state of the art narratively-identified estimates.