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Scenario Analysis with Multivariate Bayesian Machine Learning Models

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  • Michael Pfarrhofer
  • Anna Stelzer

Abstract

We present an econometric framework that adapts tools for scenario analysis, such as variants of conditional forecasts and generalized impulse responses, for use with dynamic nonparametric models. The proposed algorithms are based on predictive simulation and sequential Monte Carlo methods. Their utility is demonstrated with three applications: (1) conditional forecasts based on stress test scenarios, measuring (2) macroeconomic risk under varying financial stress, and estimating the (3) asymmetric effects of financial shocks in the US and their international spillovers. Our empirical results indicate the importance of nonlinearities and asymmetries in relationships between macroeconomic and financial variables.

Suggested Citation

  • Michael Pfarrhofer & Anna Stelzer, 2025. "Scenario Analysis with Multivariate Bayesian Machine Learning Models," Papers 2502.08440, arXiv.org, revised Nov 2025.
    • Handle: RePEc:arx:papers:2502.08440
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    References listed on IDEAS

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    1. Breitenlechner, Max & Georgiadis, Georgios & Schumann, Ben, 2022. "What goes around comes around: How large are spillbacks from US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 45-60.
    2. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    3. Regis Barnichon & Christian Matthes & Alexander Ziegenbein, 2022. "Are the Effects of Financial Market Disruptions Big or Small?," The Review of Economics and Statistics, MIT Press, vol. 104(3), pages 557-570, May.
    4. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
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    Cited by:

    1. Marcellino, Massimiliano & Pfarrhofer, Michael, 2025. "Nonparametric mixed frequency monitoring macro-at-risk," Economics Letters, Elsevier, vol. 255(C).
    2. Tony Chernis & Niko Hauzenberger & Haroon Mumtaz & Michael Pfarrhofer, 2025. "A Bayesian Gaussian Process Dynamic Factor Model," Papers 2509.04928, arXiv.org.
    3. Niko Hauzenberger Massimiliano Marcellino Michael Pfarrhofer Anna Stelzer, 2026. "Direct Gaussian Process Predictive Regressions with Mixed Frequency Data," BAFFI CAREFIN Working Papers 26265, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. Joshua C. C. Chan & Michael Pfarrhofer, 2025. "Large Bayesian VARs for Binary and Censored Variables," Papers 2506.01422, arXiv.org.

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