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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.
Book Synopsis Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity by : Mr. Paul A Austin
Download or read book Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity written by Mr. Paul A Austin and published by International Monetary Fund. This book was released on 2021-12-17 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Book Synopsis Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity by : Mr. Paul A Austin
Download or read book Using the Google Places API and Google Trends Data to Develop High Frequency Indicators of Economic Activity written by Mr. Paul A Austin and published by International Monetary Fund. This book was released on 2021-12-17 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Quarterly GDP statistics facilitate timely economic assessment, but the availability of such data are limited for more than 60 developing economies, including about 20 countries in sub-Saharan Africa as well as more than two-thirds of fragile and conflict-affected states. To address this limited data availablity, this paper proposes a panel approach that utilizes a statistical relationship estimated from countries where data are available, to estimate quarterly GDP statistics for countries that do not publish such statistics by leveraging the indicators readily available for many countries. This framework demonstrates potential, especially when applied for similar country groups, and could provide valuable real-time insights into economic conditions supported by empirical evidence.
Book Synopsis Panel Nowcasting for Countries Whose Quarterly GDPs are Unavailable by : Omer Faruk Akbal
Download or read book Panel Nowcasting for Countries Whose Quarterly GDPs are Unavailable written by Omer Faruk Akbal and published by International Monetary Fund. This book was released on 2023-08-04 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quarterly GDP statistics facilitate timely economic assessment, but the availability of such data are limited for more than 60 developing economies, including about 20 countries in sub-Saharan Africa as well as more than two-thirds of fragile and conflict-affected states. To address this limited data availablity, this paper proposes a panel approach that utilizes a statistical relationship estimated from countries where data are available, to estimate quarterly GDP statistics for countries that do not publish such statistics by leveraging the indicators readily available for many countries. This framework demonstrates potential, especially when applied for similar country groups, and could provide valuable real-time insights into economic conditions supported by empirical evidence.
Book Synopsis Forecasting Private Consumption by : Torsten Schmidt
Download or read book Forecasting Private Consumption written by Torsten Schmidt and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Google Trends have become a popular data source for social science research. We show that for small countries or sub-national regions like U.S. states, underlying sampling noise in Google Trends can be substantial. The data may therefore be unreliable for time series analysis and is furthermore frequency-inconsistent: daily data differs from weekly or monthly data. We provide a novel sampling technique along with the R-package trendecon in order to generate stable daily Google search results that are consistent with weekly and monthly queries of Google Trends. We use this new approach to construct long and consistent daily economic indices for the (mainly) German-speaking countries Germany, Austria, and Switzerland. The resulting indices are significantly correlated with traditional leading indicators, with the advantage that they are available much earlier.
Book Synopsis Constructing Daily Economic Sentiment Indices Based on Google Trends by : Vera Eichenauer
Download or read book Constructing Daily Economic Sentiment Indices Based on Google Trends written by Vera Eichenauer and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Google Trends have become a popular data source for social science research. We show that for small countries or sub-national regions like U.S. states, underlying sampling noise in Google Trends can be substantial. The data may therefore be unreliable for time series analysis and is furthermore frequency-inconsistent: daily data differs from weekly or monthly data. We provide a novel sampling technique along with the R-package trendecon in order to generate stable daily Google search results that are consistent with weekly and monthly queries of Google Trends. We use this new approach to construct long and consistent daily economic indices for the (mainly) German-speaking countries Germany, Austria, and Switzerland. The resulting indices are significantly correlated with traditional leading indicators, with the advantage that they are available much earlier.
The paper employs a spike-and-slab technique to select Google Trends categories into an augmented autoregressive model to nowcast Sri Lanka's consumer confidence. The paper then examines the benefits of incorporating Google Trends data in predicting consumer confidence in Sri Lanka: i. Increasing prediction accuracy: The Central Bank of Sri Lanka is able to forecast consumer confidence with greater certainty. ii. Reducing time lag: The Central Bank is able to make real-time prediction of consumer confidence. The first benefit results from the ability of using Google search queries to reflect consumer confidence. Google Inc. classifies search queries into categories via Google Trends service. Incorporating Google Trends data increases the predictive power of the forecast model, compared to AR(1) baseline. The second benefit arises because Google Trends data are available in real time. Economic time series, like consumer confidence, are reported infrequently, often monthly or quarterly. Using Google Trends data reducing the time lag of consumer confidence reporting.
Book Synopsis Incorporating Google Trends Data in Predicting Consumer Confidence in Sri Lanka by : Haiyang Zhang
Download or read book Incorporating Google Trends Data in Predicting Consumer Confidence in Sri Lanka written by Haiyang Zhang and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper employs a spike-and-slab technique to select Google Trends categories into an augmented autoregressive model to nowcast Sri Lanka's consumer confidence. The paper then examines the benefits of incorporating Google Trends data in predicting consumer confidence in Sri Lanka: i. Increasing prediction accuracy: The Central Bank of Sri Lanka is able to forecast consumer confidence with greater certainty. ii. Reducing time lag: The Central Bank is able to make real-time prediction of consumer confidence. The first benefit results from the ability of using Google search queries to reflect consumer confidence. Google Inc. classifies search queries into categories via Google Trends service. Incorporating Google Trends data increases the predictive power of the forecast model, compared to AR(1) baseline. The second benefit arises because Google Trends data are available in real time. Economic time series, like consumer confidence, are reported infrequently, often monthly or quarterly. Using Google Trends data reducing the time lag of consumer confidence reporting.
Book Synopsis Using Google Trends Data in Forecasting Economic Variables by : Djamil Yousefi
Download or read book Using Google Trends Data in Forecasting Economic Variables written by Djamil Yousefi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
