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Using Google Trend Data To Predict The Italian Unemployment Rate

Author

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  • Alessia Naccarato
  • Andrea Pierini
  • Stefano Falorsi

Abstract

The increased availability of online information in recent years has aroused interest as to the possibility of deriving indications on phenomena under studies. In the more specifically economic and statistical context, numerous studies suggest the use of online search data to improve the nowcasting and forecasting of the official economic indicators with a view to increasing the promptness of their circulation. In the same way, this paper puts forward a model for multiple time series that harnesses cointegration of the official time series of the Italian unemployment rate and the series of the Google Trend job offers query share to nowcast the monthly unemployment rate. Nowcasting is to be understood here as estimating the monthly unemployment rate for the month in which official survey is actually under way. The aim is thus to assess whether the use of Internet search data can improve the nowcasting of the economic indicator considered.

Suggested Citation

  • Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0203
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    References listed on IDEAS

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    Cited by:

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    2. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    3. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
    4. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).

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    More about this item

    Keywords

    multivariate time series analysis; preliminary estimates; online search data;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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