Predicting county level corn yields using deep, long, short-term memory models in the Corn Belt

dc.contributor.advisor Dermot J. Hayes Jiang, Zehui
dc.contributor.department Economics 2019-03-26T17:59:52.000 2020-06-30T03:13:51Z 2020-06-30T03:13:51Z Sat Dec 01 00:00:00 UTC 2018 2001-01-01 2018-01-01
dc.description.abstract <p>Having an accurate corn yield prediction is useful because it provides information about production and equilibrium post-harvest futures price prior to harvest. A publicly available corn yield prediction can help address emergent information asymmetry problems and, in doing so, improve price efficiency on futures markets. This paper is the first to predict corn yield using Long Short-Term Memory (LSTM), a special Recurrent Neural Network method. Our prediction is only 0.83 bushel/acre lower than actual corn yields in the Corn Belt, and is more accurate than the pre-harvest prediction from the USDA. And more importantly, our model provides a publicly available source that will contribute to eliminating the information asymmetry problem that arises from private sector crop yield prediction.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 7831
dc.identifier.contextkey 14007261
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/16824
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 21:06:36 UTC 2022
dc.subject.disciplines Agricultural and Resource Economics
dc.subject.disciplines Agricultural Economics
dc.subject.keywords Corn yield prediction
dc.subject.keywords Information asymmetry
dc.subject.keywords Long Short-Term Memory
dc.title Predicting county level corn yields using deep, long, short-term memory models in the Corn Belt
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 4c5aa914-a84a-4951-ab5f-3f60f4b65b3d Economics dissertation Doctor of Philosophy
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