Forecasting crop yields through climate variables using mixed frequency data. The case of Argentine soybeans

Abstract

This article evaluates the value of information on climate variables published in advance and at a higher frequency than the target variable of interest -crop yields- in order to get short-term forecasts. Aggregate and disaggregate climate data, alternative weighting schemes and di erent updating schemes are used to evaluate forecasting performance. This study focuses on the case of soybean yields in Argentina. Results show that models including high frequency weather data outperformed particularly during the three consecutive compaigns after 2008/09 when soybean yield decreased almost by 50%. Furthermore, forecast combinations showed a better forecasting performance than individual forecasting models.