Degree Type

Dissertation

Date of Award

2018

Degree Name

Doctor of Philosophy

Department

Economics

Major

Economics

First Advisor

Dermot J. Hayes

Abstract

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.

Copyright Owner

Zehui Jiang

Language

en

File Format

application/pdf

File Size

107 pages

Share

COinS