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Nowcasting the Unemployment Rate in Turkey : Let's ask Google

Author

Listed:
  • Meltem Gulenay Chadwick
  • Gonul Sengul
Abstract
We investigate whether Google search query data can improve nowcasting performance of the monthly nonagricultural unemployment rate for Turkey, where monthly unemployment rate is revealed with a lag of three months. To do so, we employ linear regression models and Bayesian Model Averaging Procedure in our analysis and use data from January 2005 to October 2011. We show that Google search query data is successful at nowcasting nonagricultural unemployment rate both in-sample and out-of-sample. When compared with an autoregressive benchmark model, where we allow only the lag values of the monthly unemployment rate, the best model contains principal components of Google search query data and it is 47.7% more accurate in-sample and 38.4% more accurate out-ofsample in terms of relative root mean square errors (RMSE). The best model that does not include any Google data is 34.1% more accurate insample and 29.4% more accurate out-ofsample. We also show via Harvey et al (1997) modification of the Diebold-Mariano test that models with Google search query data indeed perform statistically better than the autoregressive benchmark model.

Suggested Citation

  • Meltem Gulenay Chadwick & Gonul Sengul, 2015. "Nowcasting the Unemployment Rate in Turkey : Let's ask Google," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 15(3), pages 15-40.
    • Handle: RePEc:tcb:cebare:v:15:y:2015:i:3:p:15-40
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    Cited by:

    1. 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.
    2. Moses Tule & Taiwo Ajilore & Godday Ebuh, 2016. "A composite index of leading indicators of unemployment in Nigeria," Journal of African Business, Taylor & Francis Journals, vol. 17(1), pages 87-105, January.
    3. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    5. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    6. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    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. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    9. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    10. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    11. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    12. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    13. 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.
    14. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    15. 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.
    16. 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.
    17. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    18. Voraprapa Nakavachara & Nuarpear Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research.
    19. Bulut Levent & Dogan Can, 2018. "Google Trends and Structural Exchange Rate Models for Turkish Lira–US Dollar Exchange Rate," Review of Middle East Economics and Finance, De Gruyter, vol. 14(2), pages 1-12, August.
    20. Per Nymand-Andersen, 2016. "Big data: the hunt for timely insights and decision certainty," IFC Working Papers 14, Bank for International Settlements.
    21. Omer ZEYBEK & Erginbay UGURLU, 2014. "Nowcasting Credit Demand in Turkey with Google Trends Data," International Conference on Economic Sciences and Business Administration, Spiru Haret University, vol. 1(1), pages 333-340, December.
    22. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
    23. M. Elshendy & A. Fronzetti Colladon & E. Battistoni & P. A. Gloor, 2021. "Using four different online media sources to forecast the crude oil price," Papers 2105.09154, arXiv.org.
    24. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.

    More about this item

    Keywords

    Nowcasting; Nonagricultural unemployment rate; Bayesian model averaging; Google Trends; Linear models;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements

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