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Balanced bootstrap joint confidence bands for structural impulse response functions

Author

Listed:
  • Stefan Bruder
  • Michael Wolf

Abstract

Constructing joint confidence bands for structural impulse response functions based on a VAR model is a difficult task because of the non-linear nature of such functions. We propose new joint confidence bands that cover the entire true structural impulse response function up to a chosen maximum horizon with a prespecified probability (1 − α), at least asymptotically. Such bands are based on a certain bootstrap procedure from the multiple testing literature. We compare the finite-sample properties of our method with those of existing methods via extensive Monte Carlo simulations. We also investigate the effect of endogenizing the lag order in our bootstrap procedure on the finite-sample properties. Furthermore, an empirical application to a real data set is provided.

Suggested Citation

  • Stefan Bruder & Michael Wolf, 2017. "Balanced bootstrap joint confidence bands for structural impulse response functions," ECON - Working Papers 246, Department of Economics - University of Zurich, revised Jan 2018.
  • Handle: RePEc:zur:econwp:246
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    File URL: http://www.econ.uzh.ch/static/wp/econwp246.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Atsushi Inoue & Lutz Kilian, 2019. "The uniform validity of impulse response inference in autoregressions," Vanderbilt University Department of Economics Working Papers 19-00001, Vanderbilt University Department of Economics.
    2. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Constructing Joint Confidence Bands for Impulse Response Functions of VAR Models: A Review," Discussion Papers of DIW Berlin 1762, DIW Berlin, German Institute for Economic Research.
    3. Lenard Lieb & Stephan Smeekes, 2017. "Inference for Impulse Responses under Model Uncertainty," Papers 1709.09583, arXiv.org, revised Oct 2019.

    More about this item

    Keywords

    Bootstrap; impulse response functions; joint confidence bands; vector autoregressive process;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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