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The Usefulness of High-Frequency Alternative Data to Obtain Nowcasts for Japan’s GDP: Evidence from Credit Card Data

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  • Satoshi Urasawa

    (Kanagawa University)

Abstract

High-frequency alternative data, with their nearly real-time availability, provide timely information to track economic activity in real time. This study extends the standard dynamic factor model-based GDP nowcasting for Japan by introducing credit card data and examines how credit card data can be effectively employed in real-time nowcasting. The empirical results of a simulated real-time exercise suggest that credit card data, which accurately capture consumer spending on services, the area that was most heavily affected by the Covid-19 pandemic in Japan, in real time, carry important information and therefore improve the performance of nowcasting relative to nowcasts based on traditional monthly data only, especially at the early stage of forecasting when traditional data, which are published with a significant lag, are not yet available for the relevant quarter.

Suggested Citation

  • Satoshi Urasawa, 2023. "The Usefulness of High-Frequency Alternative Data to Obtain Nowcasts for Japan’s GDP: Evidence from Credit Card Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 191-211, September.
    • Handle: RePEc:spr:jbuscr:v:19:y:2023:i:2:d:10.1007_s41549-023-00085-1
    DOI: 10.1007/s41549-023-00085-1
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    References listed on IDEAS

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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019. "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 55-80, October.
    5. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    6. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    7. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    8. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    9. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    10. Modugno, Michele, 2013. "Now-casting inflation using high frequency data," International Journal of Forecasting, Elsevier, vol. 29(4), pages 664-675.
    11. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    12. Urasawa, Satoshi, 2014. "Real-time GDP forecasting for Japan: A dynamic factor model approach," Journal of the Japanese and International Economies, Elsevier, vol. 34(C), pages 116-134.
    13. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    14. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
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    More about this item

    Keywords

    Early GDP estimates; Real-time forecasts;

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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