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Identifying farmers' response to changes in marginal and average subsidies using deep learning

Storm, Hugo; Heckelei, Thomas; Baylis, Kathy; … - In: American journal of agricultural economics 106 (2024) 4, pp. 1544-1567

Persistent link: https://www.econbiz.de/10015166702

Machine learning in agricultural and applied economics : editor's choice

Storm, Hugo; Baylis, Kathy; Heckelei, Thomas - In: European review of agricultural economics 47 (2020) 3, pp. 849-892

Persistent link: https://www.econbiz.de/10012257916

Machine learning in agricultural economics

Baylis, Kathy; Heckelei, Thomas; Storm, Hugo - In: Handbook of agricultural economics : volume 5, (pp. 4551-4612). 2021

Persistent link: https://www.econbiz.de/10013459625

Surrogate modelling of a detailed farm-level model using deep learning

Shang, Linmei; Wang, Jifeng; Schäfer, David; Heckelei, … - In: Journal of agricultural economics : JAE 75 (2024) 1, pp. 235-260

Persistent link: https://www.econbiz.de/10014536582

Machine Learning and Big Data to Identify Market Expectations and Surprises : Evidence from Scheduled USDA Reports

Cao, An N.Q; Gebrekidan, Bisrat; Heckelei, Thomas; … - 2023

Traditionally, a predefined surprise proxy (such as the consensus errors of analyst forecasts) is used to estimate the market impact of public announcements. We instead use the post-event price movements to tease out what the market consensus must have been, and to estimate the event-day...

Persistent link: https://www.econbiz.de/10014349364

Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis

Storm, Hugo; Heckelei, Thomas; Baylis, Kathy - In: European review of agricultural economics 51 (2024) 3, pp. 589-616

Persistent link: https://www.econbiz.de/10015332849