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The Credit-Card-Services Augmented Divisia Monetary Aggregates

Author

Listed:
  • William Barnett

    (Department of Economics, University of Kansas; Center for Financial Stability, New York City; IC2 Institute, University of Texas at Austin)

  • Marcelle Chauvet

    (University of California at Riverside)

  • Danilo Leiva-Leon

    (Central Bank of Chile)

  • Liting Su

    (Department of Economics, The University of Kansas;)

Abstract

While credit cards provide transactions services, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities to assets. However, index number theory measures service flows and is based on aggregation theory, not accounting. We derive theory needed to measure the joint services of credit cards and money. We provide and evaluate two such aggregate measures having different objectives. We initially apply to NGDP nowcasting. Both aggregates are being implemented by the Center for Financial Stability, which will provide them to the public monthly, along with Bloomberg Terminals.

Suggested Citation

  • William Barnett & Marcelle Chauvet & Danilo Leiva-Leon & Liting Su, 2016. "The Credit-Card-Services Augmented Divisia Monetary Aggregates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201604, University of Kansas, Department of Economics, revised Aug 2016.
  • Handle: RePEc:kan:wpaper:201604
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    Cited by:

    1. William A. Barnett & Sohee Park, 2023. "Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 331-346, March.
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    3. Barnett, William A. & Su, Liting, 2020. "Financial Firm Production Of Inside Monetary And Credit Card Services: An Aggregation Theoretic Approach," Macroeconomic Dynamics, Cambridge University Press, vol. 24(1), pages 130-160, January.
    4. Fixler, Dennis & Zieschang, Kim, 2019. "Producing liquidity," Journal of Financial Stability, Elsevier, vol. 42(C), pages 115-135.
    5. Barnett, William A. & Liu, Jinan, 2019. "User cost of credit card services under risk with intertemporal nonseparability," Journal of Financial Stability, Elsevier, vol. 42(C), pages 18-35.
    6. William A. Barnett & Van H. Nguyen, 2021. "Constructing Divisia Monetary Aggregates for Singapore," JRFM, MDPI, vol. 14(8), pages 1-15, August.
    7. William A. Barnett & Liting Su, 2016. "Joint aggregation over money and credit card services under risk," Economics Bulletin, AccessEcon, vol. 36(4), pages 2301-2310.
    8. William A. Barnett & Kun He & Jingtong He, 2022. "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," JRFM, MDPI, vol. 15(10), pages 1-17, October.
    9. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    10. Barnett, William A. & Park, Hyun & Park, Sohee, 2021. "The Barnett Critique," MPRA Paper 108413, University Library of Munich, Germany.
    11. William Barnett & Hyun Park, 2023. "Have Credit Card Services Become Important to Monetary Aggregation? An Application of Sign Restricted Bayesian VAR," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202304, University of Kansas, Department of Economics.
    12. William Barnett & Liting Su, 2017. "Financial Firm Production Of Inside Monetary And Credit Card Services: An Aggregation Theoretic Approach1," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201707, University of Kansas, Department of Economics, revised Oct 2017.

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    More about this item

    Keywords

    Credit Cards; Money; Credit; Aggregation Theory; Index Number Theory; Divisia Index; Risk; Asset Pricing; Nowcasting; Indicators.;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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