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Forecasting and Modeling Macroeconomic Vulnerabilities in CESEE

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Listed:
  • Florian Huber
  • Josef Schreiner

Abstract

This paper develops a nonparametric multivariate model for assessing risks to macroecononomic outcomes in three major CESEE countries. Our model builds on Bayesian additive regression trees (BART) that remains agnostic on the relationship between the macro series and the lags thereof. Our model produces predictive distributions that exhibit non‐Gaussian features such as heavy tails, asymmetries, or multi‐modalities, making them suitable for policy analysis in extreme environments. We show that our BART model yields tail forecasts of output growth, inflation, and financial risks that are often more precise than the ones of a linear benchmark model. We then move on to analyze how the tails of selected macro series react to domestic and euro area–based financial condition shocks.

Suggested Citation

  • Florian Huber & Josef Schreiner, 2026. "Forecasting and Modeling Macroeconomic Vulnerabilities in CESEE," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 366-376, January.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:1:p:366-376
    DOI: 10.1002/for.70038
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    References listed on IDEAS

    as
    1. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
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    4. Michael Pfarrhofer & Anna Stelzer, 2025. "Scenario Analysis with Multivariate Bayesian Machine Learning Models," Papers 2502.08440, arXiv.org, revised Nov 2025.
    5. Todd E. Clark & Francesco Ravazzolo, 2015. "Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 551-575, June.
    6. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    7. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    8. Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-357, July.
    9. Feldkircher, Martin, 2015. "A global macro model for emerging Europe," Journal of Comparative Economics, Elsevier, vol. 43(3), pages 706-726.
    10. Dimitris Korobilis, 2021. "High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 493-504, March.
    11. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2016. "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1371-1391, November.
    12. Florian Huber & Gary Koop, 2023. "Subspace shrinkage in conjugate Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 556-576, June.
    13. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
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