Forecasting European high-growth Firms - A Random Forest Approach
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DOI: 10.1007/s10842-017-0257-0
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- Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
- Ho-Chang Chae, 2024. "In search of gazelles: machine learning prediction for Korean high-growth firms," Small Business Economics, Springer, vol. 62(1), pages 243-284, January.
- Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
- Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
- Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
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More about this item
Keywords
High-growth firms; Random forest; Forecasting; Variable importance;All these keywords.
JEL classification:
- M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
- L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
- O51 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - U.S.; Canada
- O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Statistics
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