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Combining official and Google Trends data to forecast the Italian youth unemployment rate

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  • Naccarato, Alessia
  • Falorsi, Stefano
  • Loriga, Silvia
  • Pierini, Andrea

Abstract

The increased availability of online information in recent years has aroused interest in the possibility of deriving indications for many kinds of phenomena. In the more specific economic and statistical context, numerous studies suggest the use of online search data to improve the forecasting and nowcasting of official economic indicators with a view to increasing the promptness of their circulation. The purpose of this work is to investigate if the use of big data can improve the forecasting of the youth unemployment rate – estimated in Italy on a monthly basis by the Italian National Institute of Statistics – by means of time series models. The time series used are those of the Google Trends query share for the keyword offerte di lavoro (job offers) and the official labour force survey data for the Italian youth unemployment rate since 2004. Two different models are estimated: an ARIMA model using only the official youth unemployment rate series and a VAR model combining the former series with the Google Trends query share. The results show that the use of Google Trends information leads to an average decrease in the forecast error.

Suggested Citation

  • Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
  • Handle: RePEc:eee:tefoso:v:130:y:2018:i:c:p:114-122
    DOI: 10.1016/j.techfore.2017.11.022
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    6. Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    7. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
    8. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
    9. 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.
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    16. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
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