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Statistically identified SVAR model with potentially skewed and fat-tailed errors

We introduce a structural vector autoregressive (SVAR) model with each of the mutually independent errors following a skewed generalized t-distribution that is more flexible than a Student's t-distribution typically considered. Hence, the effect of potential distributional misspecification is diminished and the identification may be substantially strengthened. Due to non-Gaussianity, the model is statistically identified, so that the plausibility of any economic identifying restrictions can be formally assessed. In an empirical application to U.S. monetary policy, the data lend support to narrative sign restrictions in identifying the monetary policy shock. In contrast to some of the previous literature, we find a strong negative response of real activity to contractionary monetary policy after a few months' delay. Furthermore, we find evidence in favor of the monetary policy shock not having a well-defined variance.