Robust Inference in Time-Varying Structural VAR Models: The DC-Cholesky Multivariate Stochastic Volatility Model

Konferenzbeitrag

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying. Simulations demonstrate that estimated covariance matrices become more divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy and in ation-gap persistence indicate that conclusions may critically hinge on a selected ordering of variables. The dynamic correlation Cholesky multivariate stochastic volatility model is proposed as a robust alternative.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2020: Gender Economics

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Business Fluctuations; Cycles ; Monetary Policy
• Subject: Model uncertainty ; Multivariate stochastic volatility ; Dynamic correlations ; Monetary policy ; Structural VAR

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hartwig, Benny
• Event: Veröffentlichung
• (who): ZBW - Leibniz Information Centre for Economics
• (where): Kiel, Hamburg
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/224528
• Last update: 10.03.2025, 11:42 AM CET