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Google Trends and Structural Exchange Rate Models for Turkish Lira–US Dollar Exchange Rate

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  • Bulut Levent

    (Valdosta State University, Harley Langdale, Jr. College of Business Administration, Department of Economics and FinanceValdosta, United States of America)

  • Dogan Can

    (College of Business and Economics, Department of Economics, Radford University, Radford, VA, USA)

Abstract

In this paper, we use Google Trends data to proxy macro fundamentals that are related to two conventional structural determination of exchange rate models: purchasing power parity model and the monetary exchange rate determination model. We assess forecasting performance of Google Trends based models against random walk null on Turkish Lira–US Dollar exchange rate for the period of January 2004 to August 2015. We offer a three-step methodology for query selection for macro fundamentals in Turkey and the US. In out-of-sample forecasting, results show better performance against no-change random walk predictions for specifications both when we use Google Trends data as the only exchange rate predictor or augment it with exchange rate fundamentals. We also find that Google Trends data has limited predictive power when used in year-on-year growth rate format.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:rmeecf:v:14:y:2018:i:2:p:12:n:3
    DOI: 10.1515/rmeef-2017-0026
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    References listed on IDEAS

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    Cited by:

    1. Svatopluk Kapounek & Zuzana Kučerová & Evžen Kočenda, 2022. "Selective Attention in Exchange Rate Forecasting," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(2), pages 210-229, May.
    2. Levent Bulut, 2018. "Google Trends and the forecasting performance of exchange rate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 303-315, April.
    3. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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    More about this item

    Keywords

    exchange rate; Google query selection; Google Trends; Meese-Rogoff Puzzle; Turkish Lira;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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