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Nowcasting economic activity with mobility data

In: Data science in central banking: applications and tools

Author

Listed:
  • Koji Takahashi
  • Kohei Matsumura
  • Yusuke Oh
  • Tomohiro Sugo

Abstract

In this paper, we develop high frequency indexes to measure sales in service industries and production activity in the manufacturing industry by using GPS mobility data from mobile applications. First, focusing on the possibility that the number of customers in service industries can be estimated using mobility data, we develop indicators to capture economic activity in amusement parks, shopping centers, and food services. We show that using GPS mobility data, it is possible to nowcast economic activity in the service industries, in real time, with a high level of precision---something which conventional statistics are largely unable to assist. In addition, by using statistical methods such as clustering, we can construct an indicator with even better nowcasting performance. Second, in the manufacturing sector we identify the locations of relatively large factories using panel data from the Economic Census for Business Activity and by utilizing hourly and daily mobility patterns such as a daytime ratio. We then construct indicators for nowcasting production based on the population in the specified areas. We find that we can nowcast production with a high level of precision for some labor-intensive industries including the transportation equipment and production machinery industries. These results suggest that mobility data are a useful tool for nowcasting macroeconomic activity in a timely manner.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Koji Takahashi & Kohei Matsumura & Yusuke Oh & Tomohiro Sugo, 2023. "Nowcasting economic activity with mobility data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:59-36
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    Cited by:

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    2. Furukawa, Kakuho & Hisano, Ryohei & Minoura, Yukio & Yagi, Tomoyuki, 2024. "A nowcasting model of industrial production using alternative data and machine learning approaches," Japan and the World Economy, Elsevier, vol. 71(C).

    More about this item

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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