Identification in structural vector autoregressive models with structural changes, with an application to U.S. monetary policy

A growing line of research makes use of structural changes and different volatility regimes
found in the data in a constructive manner to improve the identification of structural
parameters in Structural Vector Autoregressions (SVARs). A standard assumption made in
the literature is that the reduced form unconditional error covariance matrix varies while
the structural parameters remain constant. Under this hypothesis, it is possible to identify
the SVAR without needing to resort to additional restrictions. With macroeconomic data,
the assumption that the transmission mechanism of the shocks does not vary across volatility
regimes is debatable. We derive novel necessary and sufficient rank conditions for local
identification of SVARs, where both the error covariance matrix and the structural parameters
are allowed to change across volatility regimes. Our approach generalizes the existing
literature on ‘identification through changes in volatility’ to a broader framework and opens
up interesting possibilities for practitioners. An empirical illustration focuses on a small
monetary policy SVAR of the U.S. economy and suggests that monetary policy has become
more effective at stabilizing the economy since the 1980s.