Estimation and inference of the forecast error variance decomposition for set-identified SVARs
集合识别 SVAR 预测误差方差分解的估计与推断
We study estimation and inference of the Forecast Error Variance Decomposition (FEVD) in Structural Vector Autoregressions (SVARs) that are set-identified by internal and external (proxy) restrictions. The FEVD measures the importance of shocks for macroeconomic fluctuations and is thus key to business cycle analysis. We deliver three practitioner-oriented results. First, we characterize the endpoints of the FEVD as the extreme eigenvalues of a symmetric reduced-form matrix,…