Authors: | Keweloh, Sascha Alexander Hetzenecker, Stephan |
Title: | Efficiency gains in structural vector autoregressions by selecting informative higher-order moment conditions |
Language (ISO): | en |
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. |
Subject Headings: | SVAR monetary policy LASSO block-recursive non-Gaussianity efficiency |
URI: | http://hdl.handle.net/2003/40578 http://dx.doi.org/10.17877/DE290R-22447 |
Issue Date: | 2021 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_2621_SFB823_Keweloh_Hetzenecker_Neu.pdf | DNB | 1.31 MB | Adobe PDF | View/Open |
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