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Herramientas de Google para la predicción de variables económicas. Una aplicación al Índice Compuesto Coincidente de Actividad Económica de la Provincia de Santa Fe (ICASFe)

Author

Listed:
  • Ramiro Emmanuel Jorge

Abstract

El paper internaliza información proveniente de las herramientas Google Trends y Google Correlate con el objetivo de predecir de manera oportuna el valor del Índice Compuesto Coincidente de Actividad Económica de la Provincia de Santa Fe (ICASFe), indicador que se publica con dos meses de rezago. Para esto, se identifican aquellos términos cuyos patrones de búsqueda tienen mayor correlación con el ICASFe y luego se plantea un método de agregación para incorporarlos la serie target. Las estimaciones obtenidas con el modelo son contrastadas con datos reales de la serie target (ex post). Los resultados indican que las herramientas y el procedimiento adoptado permiten realizar una estimación consistente y ganar oportunidad respecto a las publicaciones oficiales.

Suggested Citation

  • Ramiro Emmanuel Jorge, 2020. "Herramientas de Google para la predicción de variables económicas. Una aplicación al Índice Compuesto Coincidente de Actividad Económica de la Provincia de Santa Fe (ICASFe)," Asociación Argentina de Economía Política: Working Papers 4360, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4360
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    More about this item

    Keywords

    Cycles; nowcast; big data; Google tools;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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