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Nowcasting Nominal GDP with the Credit-Card 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, University of Kansas;)

Abstract

While credit cards provide transactions services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. However, economic aggregation theory and index number theory measure service flows and are based on microeconomic theory, not accounting. We derive theory needed to measure the joint services of credit cards and money. Carried forward rotating balances are not included in the current period weakly separable block, since they were used for transactions services in prior periods. The theory is developed for the representative consumer, who pays interest for the services of credit cards during the period used for transactions. This interest rate is reported by the Federal Reserve as the average over all credit card accounts, including those not paying interest. Based on our derived theory, we propose an empirical measurement of the joint services of credit cards and money. These new Divisia monetary aggregates are widely relevant to macroeconomic research.1 We evaluate the ability of our money aggregate measures to nowcast nominal GDP. This is currently topical, given proposals for nominal GDP targeting, which require monthly measures of nominal GDP. The nowcasts are estimated using only real time information, as available for policy makers at the time predictions are made. We use a multivariate state space model that takes into account asynchronous information inflow, as proposed in Barnett, Chauvet, and Leiva-Leon (2016). The model considers real time information that arrives at different frequencies and asynchronously, in addition to mixed frequencies, missing data, and ragged edges. The results indicate that the proposed parsimonious model, containing information on real economic activity, inflation, and the new augmented Divisia monetary aggregates, produces the most accurate real time nowcasts of nominal GDP growth. In particular, we find that inclusion of the new aggregate in our nowcasting model yields substantially smaller mean squared errors than inclusion of the previous Divisia monetary aggregates.

Suggested Citation

  • William Barnett & Marcelle Chauvet & Danilo Leiva-Leon & Liting Su, 2016. "Nowcasting Nominal GDP with the Credit-Card Augmented Divisia Monetary Aggregates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201605, University of Kansas, Department of Economics, revised Aug 2016.
  • Handle: RePEc:kan:wpaper:201605
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    References listed on IDEAS

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    2. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    3. Barnett, William A. & Su, Liting, 2019. "Risk Adjustment Of The Credit-Card Augmented Divisia Monetary Aggregates," Macroeconomic Dynamics, Cambridge University Press, vol. 23(S1), pages 90-114, September.
    4. 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.
    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. Barnett, William A. & Su, Liting, 2017. "Data sources for the credit-card augmented Divisia monetary aggregates," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 899-910.
    7. 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.
    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. Barnett, William A. & Park, Hyun & Park, Sohee, 2021. "The Barnett Critique," MPRA Paper 108413, University Library of Munich, Germany.
    10. 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.
    11. 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.
    12. Liu Jinan & Serletis Apostolos, 2020. "Money growth variability and output: evidence with credit card-augmented Divisia monetary aggregates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-11, December.
    13. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    14. Serletis, Apostolos & Xu, Libo, 2020. "Functional monetary aggregates, monetary policy, and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    15. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    16. 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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