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Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data

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  • Paul Smith

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  • Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:3:p:263-284
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    Cited by:

    1. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    2. Harchaoui, Tarek M. & Janssen, Robert V., 2018. "How can big data enhance the timeliness of official statistics?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 225-234.
    3. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    4. 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.
    5. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    6. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    7. 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.
    8. Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    9. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    10. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    11. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    12. Blanchflower, David G. & Bryson, Alex, 2021. "The Economics of Walking About and Predicting Unemployment," GLO Discussion Paper Series 922, Global Labor Organization (GLO).
    13. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    14. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    15. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    16. Johannes Bock, 2018. "Quantifying macroeconomic expectations in stock markets using Google Trends," Papers 1805.00268, arXiv.org.
    17. 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.
    18. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
    19. Chong, Terence Tai Leung & Wu, Zhang & Liu, Yuchen, 2019. "Market Reaction to iPhone Rumors," MPRA Paper 92014, University Library of Munich, Germany.
    20. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
    21. Huijian Han & Zhiming Li & Zongwei Li, 2023. "Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
    22. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    23. Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    24. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    25. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.

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