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Electronic Transactions As High-Frequency Indicators Of Economics Activity

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  • John Galbraith
  • Greg Tkacz

Abstract

Since the advent of standard national accounts data over 60 years ago, economists have relied on monthly or quarterly data supplied by central statistical agencies for macroeconomics modelling and forecasting. However, technological advances of the past several years have resulted in new high-frequency data sources that could potentially provide more accurate and timely information on the current level of economic activity. In this paper we explore the usefulness of electronic transactions as real-time indicators of economics activity, using Canadian debit card data as an example. These data have the advantages of daily availability and teh high market penetration of debit cards. We find that (i) household transactions vary greatly according to the day of the week, peaking every Friday and falling every Sunday; (ii) debit card data can help lower consensus forecast errors for GDP growth; (iii) debit card transactions are correlated wtih Statistics Canada's revisions to GDP; (iv) high-frequency analyses of transactions around extreme events are possible, and in particular we are able to analyze expenditure patterns around the September 11 terrorist attacks and the August 2003 electrical blackout.

Suggested Citation

  • John Galbraith & Greg Tkacz, 2008. "Electronic Transactions As High-Frequency Indicators Of Economics Activity," Departmental Working Papers 2008-04, McGill University, Department of Economics.
    • Handle: RePEc:mcl:mclwop:2008-04
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    1. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    2. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    3. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    4. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    5. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions, Second Version," PIER Working Paper Archive 08-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Apr 2008.
    6. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    7. Henryk Gurgul & Marcin Suder, 2013. "Modeling of withdrawals from selected ATMs of the "Euronet" network," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 13, pages 65-82.
    8. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    9. Tut, Daniel, 2023. "FinTech and the COVID-19 pandemic: Evidence from electronic payment systems," Emerging Markets Review, Elsevier, vol. 54(C).

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    More about this item

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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