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Electronic Transactions As High-Frequency Indicators Of Economics Activity


  • John Galbraith


  • Greg Tkacz



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. Ariel Burstein & Martin Eichenbaum & Sergio Rebelo, 2005. "Large Devaluations and the Real Exchange Rate," Journal of Political Economy, University of Chicago Press, vol. 113(4), pages 742-784, August.
    2. Silver, Mick & Heravi, Saeed, 2001. "Scanner Data and the Measurement of Inflation," Economic Journal, Royal Economic Society, vol. 111(472), pages 383-404, June.
    3. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    4. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    5. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    6. Ron Borzekowski & K. Kiser Elizabeth & Ahmed Shaista, 2008. "Consumers' Use of Debit Cards: Patterns, Preferences, and Price Response," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(1), pages 149-172, February.
    7. David Humphrey & Lawrence Pulley & Jukka Vesala, 2000. "The Check's in the Mail: Why the United States Lags in the Adoption of Cost-Saving Electronic Payments," Journal of Financial Services Research, Springer;Western Finance Association, vol. 17(1), pages 17-39, February.
    8. Silver, Mick & Heravi, Saeed, 2005. "A Failure in the Measurement of Inflation: Results From a Hedonic and Matched Experiment Using Scanner Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 269-281, July.
    9. 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.
    10. Geoffrey R. Gerdes & Jack K. Walton & May X. Liu & Darrel W. Parke, 2005. "Trends in the use of payment instruments in the United States," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Spr, pages 180-201.
    11. Venkatesh Shankar & Ruth N. Bolton, 2004. "An Empirical Analysis of Determinants of Retailer Pricing Strategy," Marketing Science, INFORMS, vol. 23(1), pages 28-49, May.
    12. Judith A. Chevalier & Anil K. Kashyap & Peter E. Rossi, 2003. "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," American Economic Review, American Economic Association, vol. 93(1), pages 15-37, March.
    13. Jerry Hausman & Ephraim Leibtag, 2009. "CPI Bias from Supercenters: Does the BLS Know that Wal-Mart Exists?," NBER Chapters,in: Price Index Concepts and Measurement, pages 203-231 National Bureau of Economic Research, Inc.
    14. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters,in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
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    Cited by:

    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. 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.
    3. 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.
    4. 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.

    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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