Explainable Machine Learning and Economic Panel Data

Theo Berger ()
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Theo Berger: University of Applied Sciences Harz

Chapter Chapter 41 in Operations Research Proceedings 2022, 2023, pp 341-346 from Springer

Abstract: 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.

Keywords: Boosted trees; Interpretable machine learning; Economic data (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-24907-5_41

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DOI: 10.1007/978-3-031-24907-5_41

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