Bootstrapping Structural VARs: Avoiding a Potential Bias in Confidence Intervals for Impulse Response Functions
AbstractConstructing 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 23503.
Date of creation: Feb 2010
Date of revision:
impulse response function; structural VAR; bias; bootstrap;
Other versions of this item:
- Phillips, Kerk L. & Spencer, David E., 2011. "Bootstrapping structural VARs: Avoiding a potential bias in confidence intervals for impulse response functions," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 582-594.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-07-03 (All new papers)
- NEP-ECM-2010-07-03 (Econometrics)
- NEP-ETS-2010-07-03 (Econometric Time Series)
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