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Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning
McBride, Linden; Barrett, Christopher B.; Browne, … - In: Applied economic perspectives and policy 44 (2022) 2, pp. 879-892
Persistent link: https://www.econbiz.de/10013274051
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Predicting poverty with vegetation index
Tang, Binh; Liu, Yanyan; Matteson, David S. - In: Applied economic perspectives and policy 44 (2022) $m6, pp. 930-945
Persistent link: https://www.econbiz.de/10013274058
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Comparison of machine learning predictions of subjective poverty in rural China
Maruejols, Lucie Louise; Wang, Hanjie; Zhao, Qiran; … - In: China agricultural economic review : CAER 15 (2023) 2, pp. 379-399
Persistent link: https://www.econbiz.de/10014315341
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Nowcasting global poverty
Mahler, Daniel Gerszon; Castañeda Aguilar, R. Andrés; … - In: The World Bank economic review : WBER 36 (2022) 4, pp. 835-856
Persistent link: https://www.econbiz.de/10013460241
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Predicting football outcomes from Spanish league using machine learning models
Lewandowski, Michał; Chlebus, Marcin - 2021
Persistent link: https://www.econbiz.de/10012816703
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Predicting French SME failures : new evidence from machine learning techniques
Schalck, Christophe; Yankol-Schalck, Meryem - In: Applied economics 53 (2021) 51, pp. 5948-5963
Persistent link: https://www.econbiz.de/10012627109
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Predicting individual event attendance with machine learning : a "step-forward" approach
Nguyen, Jeremy K.; Karg, Adam; Valadkhani, Abbas; … - In: Applied economics 54 (2022) 27, pp. 3138-3153
Persistent link: https://www.econbiz.de/10013171194
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Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools
Chlebus, Marcin; Osika, Zuzanna - 2020
Persistent link: https://www.econbiz.de/10012322146
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Employing machine learning algorithms to build trading strategies with higher than risk-free returns
Uzunlu, Baris Yalin; Hussain, Syed Muzammil - In: International econometric review 12 (2020) 2, pp. 112-138
This research aims at exploring whether simple trading strategies developed using state-ofthe-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of...
Persistent link: https://www.econbiz.de/10012432999
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Using a machine learning approach and big data to augment WASDE forecasts : empirical evidence from US corn yield
Roznik, Mitchell; Mishra, Ashok K.; Boyd, Milton - In: Journal of forecasting 42 (2023) 6, pp. 1370-1384
Persistent link: https://www.econbiz.de/10014338902