Bootstrapping Impulse Responses of Structural Vector Autoregressive Models Identified Through GARCH

44 Pages Posted: 8 Aug 2018

See all articles by Helmut Lütkepohl

Helmut Lütkepohl

Free University of Berlin (FUB)

Thore Schlaak

German Institute for Economic Research (DIW Berlin)

Date Written: July 2018

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.

Keywords: Structural vector autoregression, conditional heteroskedasticity, GARCH, identification via heteroskedasticity

JEL Classification: C32

Suggested Citation

Lütkepohl, Helmut and Schlaak, Thore, Bootstrapping Impulse Responses of Structural Vector Autoregressive Models Identified Through GARCH (July 2018). DIW Berlin Discussion Paper No. 1750, Available at SSRN: https://ssrn.com/abstract=3227783

Helmut Lütkepohl (Contact Author)

Free University of Berlin (FUB)

Otto Suhr Institut for Political Science\
Ihnestrasse 21
Berlin
Germany

Thore Schlaak

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
14
Abstract Views
175
PlumX Metrics