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

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.

Selective Attention in Exchange Rate Forecasting

“…Accordingly, Bulut (2018) suggests using Google Trends data to nowcast the future exchange rate movement. Bulut and Dogan (2018) used Google Trends data for the forecasting of the USD-Turkish Lira exchange rate using two structural models (purchasing power parity and a monetary model) and found that these out-of-sample forecasts performed better compared with a random walk model.…”

Selective Attention in Exchange Rate Forecasting

“…Accordingly, Bulut (2018) suggests using Google Trends data to nowcast the future exchange rate movement. Bulut and Dogan (2018) used Google Trends data for the forecasting of the USD-Turkish Lira exchange rate using two structural models (purchasing power parity and a monetary model) and found that these out-of-sample forecasts performed better compared with a random walk model.…”