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A joint impulse response function for vector autoregressive models

Author

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  • Thomas F. P. Wiesen

    (University of Maine School of Economics)

  • Paul M. Beaumont

    (Florida State University Department of Economics)

Abstract

Many applications call for measuring the response due to shocks from several variables at once. We introduce a joint impulse response function (jIRF) that is independent of the order of the variables and allows for simultaneous shocks from multiple variables in the VAR, rather than one at a time as in the generalized IRF. The proposed jIRF controls for the cross-correlations of the several simultaneous shocks. As an application of the jIRF, we study the effect of the COVID-19 pandemic on trans-Atlantic volatility transmissions across large financial institutions and show that simply summing the generalized IRFs overestimates volatility transmissions.

Suggested Citation

  • Thomas F. P. Wiesen & Paul M. Beaumont, 2024. "A joint impulse response function for vector autoregressive models," Empirical Economics, Springer, vol. 66(4), pages 1553-1585, April.
    • Handle: RePEc:spr:empeco:v:66:y:2024:i:4:d:10.1007_s00181-023-02496-6
    DOI: 10.1007/s00181-023-02496-6
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    More about this item

    Keywords

    Coronavirus; COVID-19; Generalized IRF; Forecast error variance decomposition; International financial spillovers; Multiple shocks; Simultaneous impulses; Volatility transmissions;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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