02464cas a2200337 a 4500 AALejournalIMF022291 230101c9999 xx r poo 0 0eng d 5.00 USD 9781616355432 1018-5941 BD-DhAAL BD-DhAAL Austin, Paul. Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity / Paul Austin, Marco Marini, Alberto Sanchez, Chima Simpson-Bell. Washington, D.C. : International Monetary Fund, 2021. 1 online resource (47 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Environmental Accounts imf Google Trends Data imf Measurement and Data on National imf Methodology for Collecting, Estimating, and Organizing Microeconomic Data imf Reopening, COVID-19, High-Frequency Data and Business Register imf Marini, Marco. Sanchez, Alberto. Simpson-Bell, Chima. IMF Working Papers; Working Paper ; No. 2021/295 Full text available on IMF http://elibrary.imf.org/view/journals/001/2021/295/001.2021.issue-295-en.xml IMF e-Library