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Potential of Search Data in Assessment of Current Economic Conditions

Author

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  • Azusa Matsumoto

    (Bank of Japan)

  • Kohei Matsumura

    (Bank of Japan)

  • Noriyuki Shiraki

    (Bank of Japan)

Abstract

One of the problems in economic assessments is the time lag between economic activities and publication for most economic indicators. In order to address this issue, anecdotal information obtained from companies is often used as supplement material. In recent years, the development and spread of information and communications technology has made it possible to obtain a wide range of information with shorter lags, and led to the emergence of a technique called "nowcasting" that uses this information to forecast currently unreleased data. This paper contains a brief explanation of nowcasting and then examines a method using Internet search data that has garnered so much attention in recent years and the potential to use it in economic assessments. The paper includes an analysis of service consumption (travel) before and after the Great East Japan Earthquake and finds that travel-related search data provides valuable information for the nowcasting of outlays for travel.

Suggested Citation

  • Azusa Matsumoto & Kohei Matsumura & Noriyuki Shiraki, 2013. "Potential of Search Data in Assessment of Current Economic Conditions," Bank of Japan Research Papers 2013-04-18, Bank of Japan.
    • Handle: RePEc:boj:bojron:13-e-0418
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    File URL: http://www.boj.or.jp/en/research/brp/ron_2013/data/ron130418a.pdf
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    References listed on IDEAS

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    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    2. D'Amuri, Francesco & Marcucci, Juri, 2009. "‘Google it!’ Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
    3. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    4. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    5. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    6. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs & Constantin Bürgi, 2009. "Google Searches as a Means of Improving the Nowcasts of Key Macroeconomic Variables," Discussion Papers of DIW Berlin 946, DIW Berlin, German Institute for Economic Research.
    7. Tanya Suhoy, 2010. "Monthly Assessments of Private Consumption," Bank of Israel Working Papers 2010.09, Bank of Israel.
    8. Concha Artola & Enrique Galán, 2012. "Tracking the future on the web: construction of leading indicators using internet searches," Occasional Papers 1203, Banco de España.
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    Cited by:

    1. Chien-jung Ting & Yi-Long Hsiao & Rui-jun Su, 2022. "Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(4), pages 1-4.
    2. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    3. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.

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