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Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield

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  • Michael Keane
  • Timothy Neal

Abstract

SummaryPredicting the impact of climate change on crop yield is difficult, in part because the production function mapping weather to yield is high dimensional and nonlinear. We compare three approaches to predicting yields: (a) deep neural networks (DNNs), (b) traditional panel-data models, and (c) a new panel-data model that allows for unit and time fixed effects in both intercepts and slopes in the agricultural production function—made feasible by a new estimator called Mean Observation OLS (MO-OLS). Using U.S. county-level corn-yield data from 1950 to 2015, we show that both DNNs and MO-OLS models outperform traditional panel-data models for predicting yield, both in-sample and in a Monte Carlo cross-validation exercise. However, the MO-OLS model substantially outperforms both DNNs and traditional panel-data models in forecasting yield in a 2006–2015 holdout sample. We compare the predictions of all these models for climate change impacts on yields from 2016 to 2100.

Suggested Citation

  • Michael Keane & Timothy Neal, 2020. "Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 59-80.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:3:p:s59-s80.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa012
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    Cited by:

    1. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    2. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
    3. Timothy Neal & Michael Keane, 2020. "Climate Change and U.S. Agriculture: Accounting for Multi-dimensional Slope Heterogeneity in Production Functions," Discussion Papers 2018-08a, School of Economics, The University of New South Wales.

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