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

Magdalena Cornejo

doi:10.24215/18521649e022

Authors

Magdalena Cornejo Escuela de Gobierno, Universidad Torcuato Di Tella, Argentina y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina

DOI:

https://doi.org/10.24215/18521649e022

Keywords:

yields, forecasting, climate, mixed-frequency, 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.

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Author Biography

Magdalena Cornejo, Escuela de Gobierno, Universidad Torcuato Di Tella, Argentina y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina

Es becaria posdoctoral de CONICET y profesora en la Escuela de Gobierno en la Universidad Torcuato Di Tella. Doctora en Economía (UBA) y magister en econometría (UTDT). Actualmente su principal línea de investigación se focaliza en el estudio econométrico de los efectos del cambio climático en la economía. Sus publicaciones recientes incluyen artículos publicados en: Econometrics, International Journal of Forecasting, Review of Industrial Organization, Empirical Economics, Agricultural Economics, entre otros.

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