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Titel
Efficiency gains in structural vector autoregressions by selecting informative higher-order moment conditions / Sascha Alexander Keweloh, Stephan Hetzenecker
VerfasserKeweloh, Sascha Alexander ; Hetzenecker, Stephan
Erschienen[Dortmund] : SFB 823, 2021
Ausgabe
Elektronische Ressource
Umfang1 Online-Ressource (43 Seiten) : Diagramme
SerieDiscussion paper ; Nr. 26 (2021)
SchlagwörterStochastik / Vektor-autoregressives Modell / Nichtgaußscher Prozess
URNurn:nbn:de:hbz:6:2-1574815 
DOI10.17877/DE290R-22447 
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Efficiency gains in structural vector autoregressions by selecting informative higher-order moment conditions [1.27 mb]
Zusammenfassung

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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