Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/181631 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
Beiträge zur Jahrestagung des Vereins für Socialpolitik 2018: Digitale Wirtschaft - Session: Time Series No. D04-V3
Verlag: 
ZBW - Leibniz-Informationszentrum Wirtschaft, Kiel, Hamburg
Zusammenfassung: 
We propose to exploit stochastic volatility for statistical identification of Structural Vector Autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for Maximum Likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroskedastic SVARs. We apply the model to study the interdependence between monetary policy and stock markets. Since shocks identified by heteroskedasticity may not be economically meaningful, we exploit the framework to test conventional exclusion restrictions as well as Proxy SVAR restrictions which are overidentifying in the heteroskedastic model.
Schlagwörter: 
Structural Vector Autoregression (SVAR)
Identification via heteroskedasticity
Stochastic Volatility
Proxy SVAR
JEL: 
C32
C32
Dokumentart: 
Conference Paper

Datei(en):
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