Application of Machine Learning Methods in Econometric–Identifying Critical Variables Through Random Forest and SHAP

DOI

10.2991/978-94-6239-774-3_11How to use a DOI?

Keywords

Time-Series Analysis; Explainable Machine Learning; Econometrics

Abstract

This study integrated explainable machine learning with econometrics to analyze economic drivers of China–U.S. mobile communication device exports. A SHAP-based Random Forest approach with both time-series and resampling robust validation was employed to assess variable quality. Furthermore, dependent relationships between variables were shown through kinds of graphs of SHAP values, and false negative variables from statistical test were identified. The hybrid framework effectively captured and interpreted complex non-linear relationships often overlooked by linear regression of classical econometrics, offering a practical tool for robust variable selection and mechanisms explanation. However, the method did not produce explicit coefficient estimates, limiting traditional hypothesis testing. Overall, it demonstrated how interpretable ML can enhance empirical economic analysis where data dynamics violate classical assumptions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sirui Zhu
PY  - 2026
DA  - 2026/09/11
TI  - Application of Machine Learning Methods in Econometric–Identifying Critical Variables Through Random Forest and SHAP
BT  - Proceedings of the 2026 5th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2026
PB  - Atlantis Press
SP  - 110
EP  - 124
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-774-3_11
DO  - 10.2991/978-94-6239-774-3_11
ID  - Zhu2026
ER  -