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Forecasting urea prices

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  • Seon-Woong Kim
  • B. Wade Brorsen

Abstract

Managing urea price risk is a concern of firms in the urea supply chain due to high price volatility and relatively slow transportation. This study develops urea price forecasting models as a way to reduce price risk. The forecasting models are evaluated based on multiple accuracy measures and compared to Fertilizer Week, a commercial forecast. An autoregressive model with exogenous variables (ARX) using a window size of 48 months outperforms the other models. No statistical difference exists between our best model and Fertilizer Week. Encompassing tests show that a combination model using the two models outperforms using Fertilizer Week forecasts alone. A combined model using 66.8% of $$ Fertilzer\, Week $$FertilzerWeek and 33.2% of the ARX brings about the minimum forecast error.

Suggested Citation

  • Seon-Woong Kim & B. Wade Brorsen, 2017. "Forecasting urea prices," Applied Economics, Taylor & Francis Journals, vol. 49(49), pages 4970-4981, October.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:49:p:4970-4981
    DOI: 10.1080/00036846.2017.1296554
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