Block-recursive non-Gaussian structural vector autoregressions
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Date
2021
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Abstract
This study combines block-recursive restrictions with higher-order moment conditions to identify
and estimate non-Gaussian structural vector autoregressions. The estimator allows to impose
a block-recursive structure on the SVAR and for a given block-recursive structure we derive
a conservative set of assumptions on the dependence and Gaussianity of the shocks to ensure
identification. We use a Monte Carlo simulation to illustrate the advantages of the proposed blockrecursive
estimator compared to unrestricted, purely data driven estimators in small samples. The
block-recursive estimator is used to analyze the interdependence of monetary policy and the stock
market. We find that a positive stock market shock contemporaneously increases the nominal
interest rate, while contractionary monetary policy shocks lead to lower stock returns on impact.
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Keywords
SVAR, monetary policy, stock market, block-recursive, non-Gaussianity, identification