Structural Vector Autoregressions with Nonnormal Residuals
Title: Structural Vector Autoregressions with Nonnormal Residuals
Citation: Journal of Business & Economic Statistics, 2010, 28, 1, 159-168
In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just-identifying in a standard SVAR framework. The results are illustrated using a U.S. macro data set and a system of U.S. and European interest rates.
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