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Bootstrapping Impulse Responses of Structural Vector Autoregressive Models Identified through GARCH

Author

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  • Helmut Lütkepohl
  • Thore Schlaak

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.

Suggested Citation

  • Helmut Lütkepohl & Thore Schlaak, 2018. "Bootstrapping Impulse Responses of Structural Vector Autoregressive Models Identified through GARCH," Discussion Papers of DIW Berlin 1750, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1750
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Structural vector autoregression; conditional heteroskedasticity; GARCH; identification via heteroskedasticity;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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