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

Author

Listed:
  • Magdalena Cornejo

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

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 different 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 campaigns after 2008/09 when soybean yield decreased almost by 50%. Furthermore, forecast combinations showed a better forecasting performance than individual forecasting models.

Suggested Citation

  • Magdalena Cornejo, 2021. "Forecasting crop yields through climate variables using mixed frequency data. The case of Argentine soybeans," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 67, pages 93-106, January-D.
    • Handle: RePEc:lap:journl:632
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    File URL: https://revistas.unlp.edu.ar/Economica/article/view/10621/12171
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    More about this item

    Keywords

    yields; forecasting; climate; mixed-frequency; soybeans;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General

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