Assessing price forecasting models for organic commodities.

This project develops price forecasting models to guide organic commodity marketing decisions. Forecasting models for organic commodity prices at the farm level are specified and tested using a comprehensive national farm price series data set collected for organic commodities in the United States. A framework for testing the predictive ability of competing models using conditional predictive ability is developed. Forecast performance is evaluated using the root mean squared error (RMSE) and mean absolute error (MAE) for point forecast comparison along with tests for market timing ability of the forecasting model.