Identification of Structural Vector Autoregressions by Stochastic Volatility

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2018: Digitale Wirtschaft - Session: Time Series ; No. D04-V3

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: Structural Vector Autoregression (SVAR) ; Identification via heteroskedasticity ; Stochastic Volatility ; Proxy SVAR

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Bertsche, Dominik ; Braun, Robin
• Event: Veröffentlichung
• (who): ZBW - Leibniz-Informationszentrum Wirtschaft
• (where): Kiel, Hamburg
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/181631
• Last update: 10.03.2025, 11:45 AM CET