Small-Sample Confidence Intervals for Impulse Response Functions

Bias-corrected bootstrap confidence intervals explicitly account for the bias and skewness of the small-sample distribution of the impulse response estimator, while retaining asymptotic validity in stationary autoregressions. Monte Carlo simulations for a wide range of bivariate models show that in...

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Bibliographic Details
Published in:The review of economics and statistics 1998-05, Vol.80 (2), p.218-230
Main Author: Kilian, Lutz
Format: Article
Language:English
Subjects:
Bias
Coefficients
Confidence interval
Econometrics
Economic models
Economic theory
Estimation bias
Estimators
Macroeconomics
Modeling
Monte Carlo simulation
Musical intervals
Point estimators
Region of integration
Studies
Vector autoregression
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Description
Summary:Bias-corrected bootstrap confidence intervals explicitly account for the bias and skewness of the small-sample distribution of the impulse response estimator, while retaining asymptotic validity in stationary autoregressions. Monte Carlo simulations for a wide range of bivariate models show that in small samples bias-corrected bootstrap intervals tend to be more accurate than delta method intervals, standard bootstrap intervals, and Monte Carlo integration intervals. This conclusion holds for VAR models estimated in levels, as deviations from a linear time trend, and in first differences. It also holds for random walk processes and cointegrated processes estimated in levels. An empirical example shows that bias-corrected bootstrap intervals may imply economic interpretations of the data that are substantively different from standard methods.
ISSN:0034-6535
1530-9142
DOI:10.1162/003465398557465