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Constructing Joint Confidence Bands for Impulse Response Functions of VAR Models - A Review

Author

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  • Lütkepohl, Helmut

    (DIW Berlin and Freie Universität Berlin)

  • Staszewska-Bystrova, Anna

    (Faculty of Economics and Sociology, University of Lodz)

  • Winker, Peter

    (University of Giessen)

Abstract

Methods for constructing joint confidence bands for impulse response functions which are commonly used in vector autoregressive analysis are reviewed. While considering separate intervals for each horizon individually still seems to be the most common approach, a substantial number of methods have been proposed for making joint inferences about the complete impulse response paths up to a given horizon. A structured presentation of these methods is provided. Furthermore, existing evidence on the small-sample performance of the methods is gathered. The collected information can help practitioners to decide on a suitable confidence band for a structural VAR analysis.

Suggested Citation

  • Lütkepohl, Helmut & Staszewska-Bystrova, Anna & Winker, Peter, 2018. "Constructing Joint Confidence Bands for Impulse Response Functions of VAR Models - A Review," Lodz Economics Working Papers 4/2018, University of Lodz, Faculty of Economics and Sociology.
    • Handle: RePEc:ann:wpaper:4/2018
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    File URL: http://hdl.handle.net/11089/25920
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    Cited by:

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    2. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    3. Winker, Peter, 2023. "Visualizing Topic Uncertainty in Topic Modelling," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277584, Verein für Socialpolitik / German Economic Association.
    4. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    5. Yukang Jiang & Xueqin Wang & Zhixi Xiong & Haisheng Yang & Ting Tian, 2022. "Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model," Papers 2209.05998, arXiv.org.
    6. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).

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    More about this item

    Keywords

    Impulse responses; vector autoregressive model; joint confidence bands;
    All these keywords.

    JEL classification:

    • 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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