Vorträge des DIW Berlin

Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity

Bootstrapping Impulse Responses of Structural Vector Autoregressive Models with GARCH Residuals

Structural Vectorautoregressive Analysis

Structural Vector Autoregressive Analysis of the Global Market for Crude Oil

Inference in Partially Identified Heteroskedastic Simultaneous Equations Models

Inference in Partially Identified Heteroskedastic Simultaneous Equations Models

Estimation of Structural Impulse Responses: Short-Run versus Long-run Identifying Restrictions

Estimation of Structural Impulse Responses: Short-Run versus Long-run Identifying Restrictions

Inference in Partially Identified Heteroskedastic Simultaneous Equations Models: Keynote Lecture

Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction Between U.S. Monetary Policy and the Stock Market

Inference in Partially Identified Heteroskedastic Simultaneous Equations Models

Structural Vector Autoregressions with Heteroskedasticity: A Review of Different Volatility Models

Calculating Joint Confidence Bands for Impulse Response Functions Using Highest Density Regions

This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models. The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Monte Carlo comparison of the HDR bands with existing alternatives shows that the former are competitive...

Calculating Joint Confidence Bands for Impulse Response Functions Using Highest Density Regions

This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models. The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Monte Carlo comparison of the HDR bands with existing alternatives shows that the former are competitive...

Testing for Identification in SVAR-GARCH Models: Reconsidering the Impact of Monetary Shocks on Exchange Rates

Changes in residual volatility in vector autoregressive (VAR) models can be used for identifying structural shocks in a structural VAR analysis. Testable conditions are given for full identification for the case where the volatility changes can be modelled by a multivariate GARCH process. Formal statistical tests are presented for identification and their small sample properties are investigated...

Calculating Joint Confidence Bands for Impulse Response Functions Using Highest Density Regions

This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models. The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Monte Carlo comparison of the HDR bands with existing alternatives shows that the former are competitive...

Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

Calculating Joint Confidence Bands for Impulse Response Functions Using Highest Density Regions

This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models. The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Monte Carlo comparison of the HDR bands with existing alternatives shows that the former are competitive...

Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance, assuming known dates for variance changes is often unrealistic while more exible models based on GARCH or Markov switching residuals are difficult to handle...