Bootstrapping Impulse Responses of Structural Vector Autoregressive Models Identified through GARCH
Helmut Lütkepohl () and
No 1750, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research
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.
Keywords: Structural vector autoregression; conditional heteroskedasticity; GARCH; identification via heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 42 p.
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:diw:diwwpp:dp1750
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