Efficiency gains in structural vector autoregressions by selecting informative higher-order moment conditions
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Date
2021
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Abstract
This study combines block-recursive restrictions with non-Gaussian and mean independent shocks
to derive identifying and overidentifying higher-order moment conditions for structural vector
autoregressions. We show that overidentifying higher-order moments can contain additional
information and increase the efficiency of the estimation. In particular, we prove that in the
non-Gaussian recursive SVAR higher-order moment conditions are relevant and therefore, the
frequently applied estimator based on the Cholesky decomposition is inefficient. Even though
incorporating information in valid higher-order moments is asymptotically efficient, including
many redundant and potentially even invalid moment conditions renders standard SVAR GMM
estimators unreliable in finite samples. We apply a LASSO-type GMM estimator to select the
relevant and valid higher-order moment conditions, increasing finite sample precision. A Monte
Carlo experiment and an application to quarterly U.S. data illustrate the improved performance
of the proposed estimator.
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Keywords
SVAR, monetary policy, LASSO, block-recursive, non-Gaussianity, efficiency