Open AccessForecasting crop yields through climate variables using mixed frequency data. The case of Argentine soybeans
Author(s) -
Magdalena Cornejo
Publication year - 2021
Publication title -
económica/económica
Language(s) - English
Resource type - Journals
eISSN - 1852-1649
pISSN - 0013-0419
DOI - 10.24215/18521649e022
Subject(s) - weighting , econometrics , variable (mathematics) , yield (engineering) , aggregate (composite) , climate change , crop yield , mathematics , statistics , computer science , agronomy , ecology , medicine , mathematical analysis , materials science , biology , metallurgy , composite material , radiology
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.