Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/201419 
Year of Publication: 
2019
Series/Report no.: 
DIW Discussion Papers No. 1750
Version Description: 
Revised Version: January 29, 2019
Publisher: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Abstract: 
Different bootstrap methods and estimation techniques for inference for structural vector autoregressive (SVAR) models identified by generalized autoregressive conditional heteroskedasticity (GARCH) are reviewed and compared in a Monte Carlo study. 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. The latter method is computationally more efficient than the other methods and it is competitive with the other 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

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