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Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends

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  • Nuno Barreira
  • Pedro Godinho
  • Paulo Melo

Abstract

This work presents a study describing the use of Internet search information to achieve an improved nowcasting ability with simple autoregressive models, using data from four countries and two different application domains with social and economic significance: unemployment rate and car sales. The results we obtained differ by country/language and application area. In the case of unemployment, we find that Google Trends data lead to the improvement of nowcasts in three out of the four considered countries: Portugal, France and Italy. However, there are sometimes important differences in the predictive ability of these data when we consider different out-of-sample periods. For car sales, we find that, in some cases, the volume of search queries helps explaining the variance of the car sales data. However, we find little support for the hypothesis that search query data may improve predictions, and we present several possible reasons for these results. Taking all results into account, we conclude that, when Google Trends variables are significantly different from zero in-sample, they tend to lead to improvements in out-of-sample predictive ability. The results can have implications for nowcasting, by providing some indications regarding the advantage or not of the use of search data to improve simple models and indirectly by highlighting the sensitivity of the approach to the actual country-specific base, nowcasting period and search data. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
  • Handle: RePEc:kap:netnom:v:14:y:2013:i:3:p:129-165
    DOI: 10.1007/s11066-013-9082-8
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    References listed on IDEAS

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    Cited by:

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    2. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    3. Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
    4. 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.
    5. Dimpfl, Thomas & Langen, Tobias, 2015. "A Cross-Country Analysis of Unemployment and Bonds with Long-Memory Relations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112921, Verein für Socialpolitik / German Economic Association.
    6. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    7. 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.
    8. Gulsah Senturk, 2022. "Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 229-244, July.
    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.
    10. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    11. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    12. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
    13. Andrius Grybauskas & Vaida Pilinkienė & Mantas Lukauskas & Alina Stundžienė & Jurgita Bruneckienė, 2023. "Nowcasting Unemployment Using Neural Networks and Multi-Dimensional Google Trends Data," Economies, MDPI, vol. 11(5), pages 1-23, April.
    14. Michael Olumekor & Hossam Haddad & Nidal Mahmoud Al-Ramahi, 2023. "The Relationship between Search Engines and Entrepreneurship Development: A Granger-VECM Approach," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    15. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    16. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    17. Nymand-Andersen, Per & Pantelidis, Emmanouil, 2018. "Google econometrics: nowcasting euro area car sales and big data quality requirements," Statistics Paper Series 30, European Central Bank.
    18. N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.
    19. 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.
    20. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).

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