Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield
Year of publication: |
2020
|
---|---|
Authors: | Keane, Michael P. ; Neal, Timothy |
Published in: |
The econometrics journal. - Oxford : Oxford University Press, ISSN 1368-423X, ZDB-ID 1475536-1. - Vol. 23.2020, 3, p. S59-S80
|
Subject: | Climate change | crop yield | panel data | machine learning | deep learning | Klimawandel | Ernteertrag | Crop yield | Prognoseverfahren | Forecasting model | Neuronale Netze | Neural networks | Künstliche Intelligenz | Artificial intelligence | Agrarproduktion | Agricultural production | Panel | Panel study | Prognose | Forecast |
-
Forecasting realized volatility with machine learning : panel data perspective
Zhu, Haibin, (2023)
-
Keane, Michael P., (2020)
-
Model estimates of climate impact on grain and leguminous crops yield in the regions of Russia
Siptits, S. O., (2021)
- More ...
-
Evaluating Consumers' Choices of Medicare Part D Plans : A Study in Behavioral Welfare Economics
Keane, Michael P., (2019)
-
A Practical Guide to Weak Instruments
Keane, Michael P., (2021)
-
The Impact of Child Work on Cognitive Development : Results from Four Low to Middle Income Countries
Keane, Michael P., (2020)
- More ...