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Mr. Futoshi Narita and Rujun Yin
Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.
Mr. Futoshi Narita and Rujun Yin

data obtained through recent technology have enormous potential to fill information gaps in developing economies. We investigate how much information we could obtain from Internet search frequencies to strengthen the capacity to monitor and assess current economic developments. Our findings help us better utilize new sources of information such as Google Trendsdata in economic analyses . Useful information contained in Google’s SVI is demonstrated by the improved in-sample and out-of-sample performances of a simple forecasting model, conditional on lagged

Mr. Paul A Austin, Mr. Marco Marini, Alberto Sanchez, Chima Simpson-Bell, and James Tebrake
As the pandemic heigthened policymakers’ demand for more frequent and timely indicators to assess economic activities, traditional data collection and compilation methods to produce official indicators are falling short—triggering stronger interest in real time data to provide early signals of turning points in economic activity. In this paper, we examine how data extracted from the Google Places API and Google Trends can be used to develop high frequency indicators aligned to the statistical concepts, classifications, and definitions used in producing official measures. The approach is illustrated by use of Google data-derived indicators that predict well the GDP trajectories of selected countries during the early stage of COVID-19. To this end, we developed a methodological toolkit for national compilers interested in using Google data to enhance the timeliness and frequency of economic indicators.
Mr. Paul A Austin, Mr. Marco Marini, Alberto Sanchez, Chima Simpson-Bell, and James Tebrake

others. Jun, Yoo and Choi (2016) traces the ten years of research using Google Trends since the company made this source of data available in 2006. Noting that the availability of timely data is a long standing challenge for policy making and analysis for low-income developing countries, Narita and Yin (2018) explored the use of Google Trends data to narrow such information gaps. Many organizations have since developed timely leading indicators using Google data (Google Trends, Google Mobility data, Google APIs) that track well official measures of economic activity

Mr. Serhan Cevik

the predictive ability of Google Trends data for tourist arrivals to The Bahamas . With the spread of the Internet throughout the world, the data collected by search engines like Google allows researchers to measure the intended behavior of consumers at the individual level and take that into account in forecasting at the macroeconomic level. Furthermore, the availability of internet search data provides new high-frequency information that can potentially improve forecast accuracy. Accordingly, this paper develops an econometric model of tourist arrivals to The

Mr. Serhan Cevik
The widespread availability of internet search data is a new source of high-frequency information that can potentially improve the precision of macroeconomic forecasting, especially in areas with data constraints. This paper investigates whether travel-related online search queries enhance accuracy in the forecasting of tourist arrivals to The Bahamas from the U.S. The results indicate that the forecast model incorporating internet search data provides additional information about tourist flows over a univariate approach using the traditional autoregressive integrated moving average (ARIMA) model and multivariate models with macroeconomic indicators. The Google Trends-augmented model improves predictability of tourist arrivals by about 30 percent compared to the benchmark ARIMA model and more than 20 percent compared to the model extended only with income and relative prices.
Mr. Paul A Austin, Mr. Marco Marini, Alberto Sanchez, Chima Simpson-Bell, and James Tebrake

Copyright Page © 2021 International Monetary Fund WP/21/295 IMF Working Paper Statistics Department Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity Prepared by Paul Austin, Marco Marini, Alberto Sanchez, Chima Simpson-Bell, and James Tebrake Authorized for distribution by J. R. Rosales December 2021 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate . The views expressed in IMF Working Papers are those of

Olga Bespalova

Annex 1. Notes on Google Trends Data . Annex 2. Panel Charts: Dynamics in Data Annex 3. Main Emirical Results Annex 4. Panel Charts: Projected Arrivals Annex 5. Robustness Check References FIGURES Figure 1. Tourist Arrivals to Aruba: Dynamics, Sources of Origin, and Macroeconomic Impact Figure 2. Role of Regressors and Model Terms in the RMSE improvement Figure 3. In-Sample Tourist Arrivals Projections Figure A2-1. Dynamics in Tourist Arrivals and Explanatory Variables Figure A4-1. Projected Arrivals: Comparison across ARIMA classes, before and