Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Review of Middle East Economics and Finance

Ed. by Dibeh, Ghassan / Assaf, Ata / Cobham, David / Hakimian, Hassan / Henry, Clement M.

See all formats and pricing
More options …

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

Levent BulutORCID iD: http://orcid.org/0000-0003-1662-2050 / Can Dogan
Published Online: 2018-08-10 | DOI: https://doi.org/10.1515/rmeef-2017-0026


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.

Keywords: exchange rate; Google query selection; Google Trends; Meese-Rogoff Puzzle; Turkish Lira

JEL Classification: C53; F31; F37


  • Algan, N., and S. Gencer. 2011. “Türkiye’de para talebi fonksiyonunun modellenmesi.” Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi 20 (1): 195–212.Google Scholar

  • Altıntas, H. 2008. “Türkiye’de para talebinin istikrarı ve sınır testi yaklasımıyla öngörülmesi: 1985-2006.” Erciyes Üniversitesi İİBF Dergisi 30: 15–46.Google Scholar

  • Askitas, N., and K. F. Zimmermann. 2009. “Google Econometrics and Unemployment Forecasting.” Applied Economics Quarterly 55 (2): 107–120.CrossrefGoogle Scholar

  • Bacchetta, Philippe, Eric Van Wincoop, and Toni Beutler. 2010. “Can Parameter Instability Explain the Meese-Rogoff Puzzle?.” In NBER International Seminar on Macroeconomics 2009, 125–173. University of Chicago Press.Google Scholar

  • Bulut, Levent. 2018. “Google Trends and the Forecasting Performance of Exchange Rate Models.” Journal of Forecasting, 37:303–315. https://doi.org/10.1002/for.2500Web of ScienceCrossref

  • Chadwick, M.G., and G. Şengül. 2015. “Nowcasting the Unemployment Rate in Turkey: Let's Ask Google.” Central Bank Review 1: 1.Google Scholar

  • Chen, T., E.P.K. So, L. Wu, and I.K.M. Yan. 2015. “The 2007–2008 US Recession: What Did the Real-Time Google Trends Data Tell the United States?.” Contemporary Economic Policy 33 (2): 395–403.CrossrefWeb of ScienceGoogle Scholar

  • Choi, H., and H. Varian. 2009. “Predicting Initial Claims for Unemployment Benefits.” Google Incorporating (2009): 1–5.Google Scholar

  • Choi, H., and H. Varian. 2012. “Predicting the Present with Google Trends.” Economic Record 88 (s1): 2–9.Web of ScienceCrossrefGoogle Scholar

  • Civcir, Irfan 2003. “The Monetary Model of the Exchange Rate under High Inflation – The Case of the Turkish Lira/US Dollar.” Czech Journal of Economics and Finance (Finance a Uver) 53 (3–4): 113–129.Google Scholar

  • Clark, T.E., and K.D. West. 2006. “Using Out-Of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis.” Journal of Econometrics 135 (1): 155–186.CrossrefGoogle Scholar

  • Clark, T.E., and K.D. West. 2007. “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics 138 (1): 291–311.Web of ScienceCrossrefGoogle Scholar

  • Diebold, F.X., and R.S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13: 253–263.Google Scholar

  • Diebold, F.X., and J.A. Nason. 1990. “Nonparametric Exchange Rate Prediction?.” Journal of International Economics 28 (3–4): 315–3.CrossrefGoogle Scholar

  • Engel, C., N.C. Mark, and K.D. West. 2007. “Exchange Rate Models are Not as Bad as You Think (No. W13318).” National Bureau of Economic Research 22: 381–441.Google Scholar

  • Faust, J., J.H. Rogers, and J.H. Wright. 2003. “Exchange Rate Forecasting: The Errors We've Really Made.” Journal of International Economics 60 (1): 35–59.CrossrefGoogle Scholar

  • Gourinchas, P.O., and H. Rey. 2007. “International Financial Adjustment.” Journal of Political Economy 115 (4): 665–703.CrossrefGoogle Scholar

  • Hoffman, D. L., and R. H. Rasche. 1991. “Long-Run Income and Interest Elasticities of Money Demand in the United States.” The Review of Economics and Statistics 73 (4): 665–674.CrossrefGoogle Scholar

  • Koop, G., and L. Onorante. 2013. “Macroeconomic Nowcasting Using Google Probabilities.” University of Strathclyde.Google Scholar

  • Mark, N.C. 1995 . “Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability.” The American Economic Review 85 (1): 201– 218.Google Scholar

  • Meese, R.A., and K. Rogoff. 1983. “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?.” Journal of International Economics 14 (1): 3–24.CrossrefGoogle Scholar

  • Meese, R.A., and A.K. Rose. 1991. “An Empirical Assessment of Non-Linearities in Models of Exchange Rate Determination.” The Review of Economic Studies 58 (3): 603–619.CrossrefGoogle Scholar

  • Mohebbi, Matt, Dan Vanderkam, Julia Kodysh, Rob Schonberger, Hyunyoung Choi, and Sanjiv Kumar (2011) “Google Correlate Whitepaper.” https://www.google.com/trends/correlate/whitepaper.pdf.

  • Molodtsova, T., and D.H. Papell. 2009. “Out-Of-Sample Exchange Rate Predictability with Taylor Rule Fundamentals.” Journal of International Economics 77 (2): 167–180.CrossrefWeb of ScienceGoogle Scholar

  • Nikolsko-Rzhevskyy, Alex, and Ruxandra Prodan. 2012. “Markov Switching and Exchange Rate Predictability.” International Journal of Forecasting 28 (2): 353–365.Web of ScienceCrossrefGoogle Scholar

  • Rogoff, Kenneth S., and Vania Stavrakeva. 2008. “The Continuing Puzzle of Short Horizon Exchange Rate Forecasting. No. W14071.” National Bureau of Economic Research.Google Scholar

  • Scott, S.L., and H.R. Varian. 2014. “Predicting the Present with Bayesian Structural Time Series.” International Journal of Mathematical Modelling and Numerical Optimisation 5 (1–2): 4–23.CrossrefGoogle Scholar

  • Seabold, S., and A. Coppola. 2015. “Nowcasting Prices Using Google Trends: An Application to Central America.” World Bank Policy Research Working Paper 7398.Google Scholar

  • Theil, H. 1966. Applied Economic Forecasting. Amsterdam. Netherlands: North‐Holland.Google Scholar

  • Tumturk, O. 2017. “Stability of Money Demand Function in Turkey.” Business and Economics Research Journal 8 (1): 35.CrossrefGoogle Scholar

  • Vosen, S., and T. Schmidt. 2011. “Forecasting Private Consumption: Survey-Based Indicators Vs. Google Trends.” Journal of Forecasting 30 (6): 565–578.CrossrefWeb of ScienceGoogle Scholar

  • Wang, J., and J.J. Wu. 2012. “The Taylor Rule and Forecast Intervals for Exchange Rates.” Journal of Money, Credit and Banking 44 (1): 103–144.CrossrefWeb of ScienceGoogle Scholar

  • West, K.D. 1996 . “Asymptotic Inference about Predictive Ability.” Econometrica: Journal of the Econometric Society 64 (5): 1067– 1084.CrossrefGoogle Scholar

  • Zeybek, O., and E. Ugurlu. 2015. “Nowcasting Credit Demand in Turkey with Google Trends Data.” Journal of Applied Economic Science 10 (2(32)): 293–300.Google Scholar

About the article

Published Online: 2018-08-10

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK), Grant Number: 115C089

Citation Information: Review of Middle East Economics and Finance, Volume 14, Issue 2, 20170026, ISSN (Online) 1475-3693, DOI: https://doi.org/10.1515/rmeef-2017-0026.

Export Citation

© 2018 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in