IDEAS home Printed from https://ideas.repec.org/a/icb/wpaper/v1y2014i1333-340.html
   My bibliography  Save this article

Nowcasting Credit Demand in Turkey with Google Trends Data

Author

Listed:
  • Omer ZEYBEK

    (ING Bank Turkey Analytic CRM Dept.,)

  • Erginbay UGURLU

    (Hitit Universitesi, FEAS, Department of Economics)

Abstract

Age of Big Data and internet has brought variety of opportunities for social researchers on identifying on-going social trends instantly. As internet user base grew exponentially, major internet content search companies have begun to offer data mining products which could extract attitude of on-going trends and identify new trends on web as well. Since 2009, as a pioneer on these web analytics solutions Google has launched Google Trends service, which enables to researchers to examine change of trend on specific keywords. We use weekly Google Trends Index of 'General Purpose Loan' (GT) and total out-standing volume of Turkish banking system from the data period of first week of March 2011 to second week of September 2014. In this paper we test whether the Google Analytics search index series can be used as a consistent forecaster of national general purpose loan (GPL] demand in Turkey. We show how to use search engine data to forecast Turkish GPL demand. The results show that Google search query data is successful at nowcasting GPL demand.

Suggested Citation

  • 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.
    • Handle: RePEc:icb:wpaper:v:1:y:2014:i:1:333-340
    as

    Download full text from publisher

    File URL: http://icesba.eu/RePEc/icb/wpaper/ICESBA2014_41ZEYBEK_P333-340.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Peter Kuhn & Mikal Skuterud, 2004. "Internet Job Search and Unemployment Durations," American Economic Review, American Economic Association, vol. 94(1), pages 218-232, March.
    3. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Narcı, Muhammed Talha, 2017. "Consumer Behavior and Social Media Marketing: A Research on University Student," Bulletin of Economic Theory and Analysis, BETA Journals, vol. 2(3), pages 279-307, July-Sept.
    2. Voraprapa Nakavachara & Nuarpear Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    2. 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.
    3. 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.
    4. Yi Li, 2020. "Internet Development and Structural Transformation: Evidence from China," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 10(1), pages 1-8.
    5. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    6. Pedro Afonso Fernandes, 2024. "Forecasting with Neuro-Dynamic Programming," Papers 2404.03737, arXiv.org.
    7. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    8. Oyer, Paul & Schaefer, Scott, 2011. "Personnel Economics: Hiring and Incentives," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 20, pages 1769-1823, Elsevier.
    9. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.
    10. Aleksandra Riedl & Julia Wörz, 2018. "A simple approach to nowcasting GDP growth in CESEE economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/18, pages 56-74.
    11. Matteo Luciani & Lorenzo Ricci, 2014. "Nowcasting Norway," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 215-248, December.
    12. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    13. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
    14. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    15. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    16. Gürtzgen, Nicole & (né Nolte), André Diegmann & Pohlan, Laura & van den Berg, Gerard J., 2021. "Do digital information technologies help unemployed job seekers find a job? Evidence from the broadband internet expansion in Germany," European Economic Review, Elsevier, vol. 132(C).
    17. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.
    18. Czernich, Nina, 2014. "Does broadband internet reduce the unemployment rate? Evidence for Germany," Information Economics and Policy, Elsevier, vol. 29(C), pages 32-45.
    19. Stefania Marcassa, 2014. "Unemployment Duration of Spouses: Evidence From France," LABOUR, CEIS, vol. 28(4), pages 399-429, December.
    20. Ehrenfried, Felix & Holzner, Christian, 2019. "Dynamics and endogeneity of firms’ recruitment behaviour," Labour Economics, Elsevier, vol. 57(C), pages 63-84.

    More about this item

    Keywords

    Nowcasting web analytics; forecasting; general purpose loan.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:icb:wpaper:v:1:y:2014:i:1:333-340. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rocsana Bucea-Manea-Tonis (email available below). General contact details of provider: http://icesba.eu .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.