Bootstrapping Structural VARs: Avoiding a Potential Bias in Confidence Intervals for Impulse Response Functions

Kerk L. Phillips and David E. Spencer

MPRA Paper from University Library of Munich, Germany

Abstract: Constructing bootstrap confidence intervals for impulse response functions (IRFs) from structural vector autoregression (SVAR) models has become standard practice in empirical macroeconomic research. The accuracy of such confidence intervals can deteriorate severely, however, if the bootstrap IRFs are biased. In this paper, we document an apparently common source of bias in the estimation of the VAR error covariance matrix. The bias is easily corrected with a straightforward scale adjustment. This bias is often unrecognized because it only affects the bootstrap estimates of the error variance, not the original OLS estimates. Nevertheless, as we illustrate here, analytically, with sampling experiments, and in an example from the literature, the bootstrap error variance bias can have significant distorting effects on bootstrap IRF confidence intervals even if the original IRF estimate relies on unbiased parameter estimates.

Keywords: impulse response function; structural VAR; bias; bootstrap (search for similar items in EconPapers)
JEL-codes: C32 E32 E37 (search for similar items in EconPapers)
Date: 2010-02
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://mpra.ub.uni-muenchen.de/23503/1/MPRA_paper_23503.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/25432/2/MPRA_paper_25432.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/38250/1/MPRA_paper_38250.pdf revised version (application/pdf)

Related works:
Journal Article: Bootstrapping structural VARs: Avoiding a potential bias in confidence intervals for impulse response functions (2011)
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