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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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  • Timothy Neal

    () (UNSW School of Economics)

  • Michael Keane

    () (UNSW School of Economics)

Abstract

Predicting 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: (i) deep neural networks (DNNs), (ii) traditional panel-data models, and (iii) 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 developed by Keane and Neal (2020) called MO-OLS. Using U.S. county-level corn yield data from 1950-2015, 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 2006-15 holdout sample. We compare predictions of all these models for climate change impacts on yields from 2016 to 2100.

Suggested Citation

  • Timothy Neal & Michael Keane, 2020. "Comparing Deep Neural Network and Econometric Approaches to Predicting the Impact of Climate Change on Agricultural Yield," Discussion Papers 2020-02, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2020-02
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    1. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    2. 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.
    3. Marshall Burke & Kyle Emerick, 2016. "Adaptation to Climate Change: Evidence from US Agriculture," American Economic Journal: Economic Policy, American Economic Association, vol. 8(3), pages 106-140, August.
    4. Kentaro Kawasaki & Shinsuke Uchida, 2016. "Quality Matters More Than Quantity: Asymmetric Temperature Effects on Crop Yield and Quality Grade," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 1195-1209.
    5. Elizabeth Marshall, & Marcel Aillery, & Scott Malcolm, & Ryan Williams,, 2015. "Climate Change, Water Scarcity, and Adaptation in the U.S. Fieldcrop Sector," Economic Research Report 262203, United States Department of Agriculture, Economic Research Service.
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    Cited by:

    1. 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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    More about this item

    Keywords

    Climate Change; Crop Yield; Panel Data; Machine Learning; Neural Net;
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