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Google Correlate and Google Trends as Nowcasting Tools for Retail Sales

Author

Listed:
  • María Florencia Camusso

    (Centro de Estudios y Servicios de la Bolsa de Comercio de Santa Fe, Universidad Nacional del Litoral)

  • Ramiro Emmanuel Jorge

    (Centro de Estudios y Servicios de la Bolsa de Comercio de Santa Fe, Universidad Nacional del Litoral)

Abstract

The paper proposes a nowcasting model for Santa Fe’s supermarkets retail sales, an indicator that is released within two months of delay, internalizing information from Google Trends and Google Correlate. The procedure identifies an array of proxy variables with high predictive ability and then uses the data in order to estimate the target series considering searching patterns. Estimations computed by the model are compared to X13-ARIMA-SEATS’s forecasts. Obtained output suggests that results are not only consistent but also more opportune that official statistical releases.

Suggested Citation

  • María Florencia Camusso & Ramiro Emmanuel Jorge, 2021. "Google Correlate and Google Trends as Nowcasting Tools for Retail Sales," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(76), pages 26-45, May.
    • Handle: RePEc:bcr:ensayo:v:1:y:2021:i:76:p:26-45
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    More about this item

    Keywords

    big data; cycles; Google tools; nowcast;
    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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