Abstract
We propose a blended approach which combines identification via heteroskedasticity with the widely used methods of sign restrictions, narrative restrictions, and external instruments. Since heteroskedasticity in the reduced form can be exploited to point identify a set of orthogonal shocks, its use results in a sharp reduction of the potentially large identified sets stemming from the typical approaches. Conversely, the identifying information in the form of sign and narrative restrictions or external instruments offers a natural solution to the labeling problem inherent in identification through heteroskedasticity. As a result, we argue that blending these methods together resolves their respective key issues and leverages their advantages, which allows to sharpen identification at virtually no cost. We illustrate the blending approach using several examples taken from recent and influential literature. Specifically, we consider labour market shocks, oil market shocks, monetary and fiscal policy shocks, and find that their effects can be rather different from what previously obtained with simpler identification strategies.
Keywords: SVAR, Identification, Heteroskedasticity, Sign restrictions, Proxy variables.