An Introduction to Machine Learning for Panel Data
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DOI: 10.1007/s11294-021-09815-6
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Cited by:
- Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
- James Ming Chen & Mira Zovko & Nika Šimurina & Vatroslav Zovko, 2021. "Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM 2.5 Pollution," IJERPH, MDPI, vol. 18(16), pages 1-59, August.
- Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
- Evaggelia Siopi & Thomas Poufinas & James Ming Chen & Charalampos Agiropoulos, 2023. "Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(1), pages 15-30, May.
- Charalampos Agiropoulos & Georgios Galanos & Thomas Poufinas, 2021. "Entrepreneurship, Income Inequality and Public Spending: A Spatial Analysis into Regional Determinants of Growing Firms in Greece," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 27(3), pages 197-218, August.
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More about this item
Keywords
Machine learning; Bias-variance tradeoff; Decision trees; Random forests; Extra trees; XGBoost; Learning ensembles; Boosting; Support vector machines; Neural networks;All these keywords.
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