Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/181035 
Is replaced by the following version: 
Title: 

Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH

The document was removed on behalf of the author(s)/ the editor(s).

Year of Publication: 
2018
Series/Report no.: 
DIW Discussion Papers No. 1750
Publisher: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Abstract: 
Different bootstrap methods and estimation techniques for inference for structural vector autoregressive (SVAR) models identified by conditional heteroskedasticity are reviewed and compared in a Monte Carlo study. The model is a SVAR model with generalized autoregressive conditional heteroskedastic (GARCH) innovations. The bootstrap methods considered are a wild bootstrap, a moving blocks bootstrap and a GARCH residual based bootstrap. Estimation is done by Gaussian maximum likelihood, a simplified procedure based on univariate GARCH estimations and a method that does not re-estimate the GARCH parameters in each bootstrap replication. It is found that the computationally most efficient method is competitive with the computationally more demanding methods and often leads to the smallest confidence sets without sacrificing coverage precision. An empirical model for assessing monetary policy in the U.S. is considered as an example. It is found that the different inference methods for impulse responses lead to qualitatively very similar results.
Subjects: 
structural vector autoregression
conditional heteroskedasticity
GARCH
identification via heteroskedasticity
JEL: 
C32
Document Type: 
Working Paper

Files in This Item:
The document was removed on behalf of the author(s)/ the editor(s) on: August 6, 2019


Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.