Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions

Daniel Grabowski (), Anna Staszewska-Bystrova and Peter Winker
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Daniel Grabowski: University of Giessen

AStA Advances in Statistical Analysis, 2020, vol. 104, issue 1, No 2, 5-32

Abstract: Abstract Inference on impulse response functions from vector autoregressive models is commonly done using bootstrap methods. These methods can be inaccurate in small samples and for persistent processes. This article investigates the construction of skewness-adjusted confidence intervals and joint confidence bands for impulse responses with improved small sample performance. We suggest to adjust the skewness of the bootstrap distribution of the autoregressive coefficients before the impulse response functions are computed. Using extensive Monte Carlo simulations, the approach is shown to improve the coverage accuracy in small- and medium-sized samples and for unit-root processes.

Keywords: Bootstrap; Confidence intervals; Joint confidence bands; Vector autoregression; C15; C32 (search for similar items in EconPapers)
Date: 2020
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Related works:
Working Paper: Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions (2018)
Working Paper: Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions (2018)
Working Paper: Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions (2018)
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DOI: 10.1007/s10182-018-00347-9

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