Estimation and Inference of the Forecast Error Variance Decomposition for Set-Identified SVARs

Francesco Fusari, Joe Marlow and Alessio Volpicella ()
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Francesco Fusari: Newcastle University Business School
Joe Marlow: University of Surrey

No 424, School of Economics Discussion Papers from School of Economics, University of Surrey

Abstract: We study the Structural Vector Autoregressions (SVARs) that impose internal and external restrictions to set-identify the Forecast Error Variance Decomposition (FEVD). This object measures the importance of shocks for macroeconomic fluctuations and is therefore of first-order interest in business cycle analysis. We make the following contributions. First, we characterize the endpoints of the FEVD as the extreme eigenvalues of a symmetric reduced-form matrix. A consistent plug-in estimator naturally follows. Second, we use the perturbation theory to prove that the endpoints of the FEVD are differentiable. Third, we construct confidence intervals that are uniformly consistent in level and have asymptotic Bayesian interpretation. We also describe the conditions to derive uniformly consistent confidence intervals for impulse responses. A Monte-Carlo exercise demonstrates the approach properties in finite samples. An unconventional monetary policy application illustrates our toolkit.e of the cost of sovereign default, capturing the FDI activity of small firms better.

JEL-codes: F13 F21 F34 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2024-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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