 Forecasting bulk prices of Bordeaux wines using leading indicatorsPost-Print from HAL Abstract: Agricultural price forecasting has been being abandoned progressively by researchers ever since the development of large-scale agricultural futures markets. However, as with many other agricultural goods, there is no futures market for wine. This paper draws on the agricultural prices forecasting literature to develop a forecasting model for bulk wine prices. The price data include annual and monthly series for various wine types that are produced in the Bordeaux region. The predictors include several leading economic indicators of supply and demand shifts. The stock levels and quantities produced are found to have the highest predictive power. The preferred annual and monthly forecasting models outperform naive random walk forecasts by 27.1% and 3.4% respectively; their mean absolute percentage errors are 2.7% and 3.4% respectively. A simple trading strategy based on monthly forecasts is estimated to increase profits by 3.3% relative to a blind strategy that consists of always selling at the spot price. Keywords: Price forecasting; Agriculture; Error correction models; Kalman filter; ARIMA models; Combining forecasts (search for similar items in EconPapers) Published in International Journal of Forecasting, 2020, 36 (2), pp.292-309. ⟨10.1016/j.ijforecast.2019.04.021⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02408202 DOI: 10.1016/j.ijforecast.2019.04.021 Access Statistics for this paper More papers in Post-Print from HAL |
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