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Nowcasting Credit Demand in Turkey with Google Trends Data


  • Omer ZEYBEK

    () (ING Bank Turkey Analytic CRM Dept.,)

  • Erginbay UGURLU

    () (Hitit Universitesi, FEAS, Department of Economics)


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

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    References listed on IDEAS

    1. Meltem Gulenay Chadwick & Gonul Sengul, 2012. "Nowcasting Unemployment Rate in Turkey : Let's Ask Google," Working Papers 1218, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    2. 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.
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    Cited by:

    1. repec:ris:betajl:0017 is not listed on IDEAS
    2. Voraprapa Nakavachara & Nuarpear Warn Lekfuangfu, 2017. "Predicting the Present Revisited: The Case of Thailand," PIER Discussion Papers 70, Puey Ungphakorn Institute for Economic Research, revised Oct 2017.

    More about this item


    Nowcasting web analytics; forecasting; general purpose loan.;

    JEL classification:

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


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