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Uncertainty quantification and global sensitivity analysis for economic models

Author

Listed:
  • Daniel Harenberg
  • Stefano Marelli
  • Bruno Sudret
  • Viktor Winschel

Abstract

We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose variance‐decomposition‐based Sobol' indices to establish an importance ranking of parameters and univariate effects to determine the direction of their impact. We employ the state‐of‐the‐art approach of constructing a polynomial chaos expansion of the model, from which Sobol' indices and univariate effects are then obtained analytically, using only a limited number of model evaluations. We apply this analysis to several quantities of interest of a standard real‐business‐cycle model and compare it to traditional local sensitivity analysis approaches. The results show that local sensitivity analysis can be very misleading, whereas the proposed method accurately and efficiently ranks all parameters according to importance, identifying interactions and nonlinearities.

Suggested Citation

  • Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    • Handle: RePEc:wly:quante:v:10:y:2019:i:1:p:1-41
    DOI: 10.3982/QE866
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    Cited by:

    1. Gersbach, Hans & Liu, Yulin & Tischhauser, Martin, 2021. "Versatile forward guidance: escaping or switching?," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    2. Yongyang Cai, 2020. "The Role of Uncertainty in Controlling Climate Change," Papers 2003.01615, arXiv.org, revised Oct 2020.
    3. Xueping Chen & Yujie Gai & Xiaodi Wang, 2023. "A-optimal designs for non-parametric symmetrical global sensitivity analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(2), pages 219-237, February.
    4. Edouard Dossetto & Christophe Chorro, 2022. "Building a global sensitivity analysis to quantify the robustness of macro-economic models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-05559166, HAL.
    5. Philipp Eisenhauer & Lena Janys & Christopher Walsh & Janós Gabler, 2023. "Structural Models for Policy-Making," CRC TR 224 Discussion Paper Series crctr224_2023_484, University of Bonn and University of Mannheim, Germany.
    6. Thomas H. Jørgensen, 2023. "Sensitivity to Calibrated Parameters," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 474-481, March.
    7. Daniel Fehrle & Christopher Heiberger & Johannes Huber, 2025. "Polynomial Chaos Expansion: Efficient Evaluation and Estimation of Computational Models," Computational Economics, Springer;Society for Computational Economics, vol. 65(2), pages 1083-1146, February.
    8. Zahir Barahmand & Marianne S. Eikeland, 2022. "Techno-Economic and Life Cycle Cost Analysis through the Lens of Uncertainty: A Scoping Review," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    9. Raputsoane, Leroi, 2025. "Economic sensitivity nexus and the minerals industry," MPRA Paper 124786, University Library of Munich, Germany.
    10. repec:ehl:lserod:117608 is not listed on IDEAS
    11. Philipp Eisenhauer & Janos Gabler & Lena Janys, 2021. "Structural Models for Policy-Making: Coping with Parametric Uncertainty," ECONtribute Discussion Papers Series 082, University of Bonn and University of Cologne, Germany.
    12. Eisenhauer, Philipp & Gabler, Janos & Janys, Lena, 2021. "Structural Models for Policy-Making: Coping with Parametric Uncertainty," IZA Discussion Papers 14317, IZA Network @ LISER.
    13. Philipp Eisenhauer & Jano's Gabler & Lena Janys & Christopher Walsh, 2021. "Structural models for policy-making: Coping with parametric uncertainty," Papers 2103.01115, arXiv.org, revised Jun 2022.
    14. Miftakhova, Alena, 2021. "Global sensitivity analysis for optimal climate policies: Finding what truly matters," Economic Modelling, Elsevier, vol. 105(C).

    More about this item

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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