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Explainable Machine Learning and Economic Panel Data

In: Operations Research Proceedings 2022

Author

Listed:
  • Theo Berger

    (University of Applied Sciences Harz
    University of Bremen)

Abstract

We apply boosted trees and Shapley values to analyze economic spillover effects within a customer-supplier network and assess economic interpretability. We translate conditional volatility into a Value-at-Risk universe and generate innovative economic features based on Natural Language Processing. Our results provide evidence for the economic relevance of spillover within a customer-supplier network for applied risk measurement. Furthermore, we demonstrate that the application of machine learning to panel data leads to innovative insights.

Suggested Citation

  • Theo Berger, 2023. "Explainable Machine Learning and Economic Panel Data," Lecture Notes in Operations Research, in: Oliver Grothe & Stefan Nickel & Steffen Rebennack & Oliver Stein (ed.), Operations Research Proceedings 2022, chapter 0, pages 341-346, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-24907-5_41
    DOI: 10.1007/978-3-031-24907-5_41
    as

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