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Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates

Author

Listed:
  • William Barnett

    (Department of Economics, University of Kansas and Center for Financial Stability, New York City)

  • Sohee Park

    (Department of Economics, University of Kansas)

Abstract

This paper investigates the performance of the Credit-Card-Augmented Divisia monetary aggregates in forecasting U.S. inflation and output growth at the 12-month horizon. We compute recursive and rolling out-of-sample forecasts using an Autoregressive Distributed Lag (ADL) model based on Divisia monetary aggregates. We use the three available versions of those monetary aggregate indices, including the original Divisia aggregates, the credit card-augmented Divisia, and the credit-card-augmented Divisia inside money aggregates. The source of each is the Center for Financial Stability (CFS). We find that the smallest Root Mean Square Forecast Errors (RMSFE) are attained with the credit-card-augmented Divisia indices used as the forecast indicators. We also consider Bayesian vector autoregression (BVAR) for forecasting annual inflation and output growth.

Suggested Citation

  • William Barnett & Sohee Park, 2021. "Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202120, University of Kansas, Department of Economics, revised Oct 2021.
    • Handle: RePEc:kan:wpaper:202120
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    File URL: http://www2.ku.edu/~kuwpaper/2021Papers/202120.pdf
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    References listed on IDEAS

    as
    1. 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.
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    6. 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.
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    11. Schunk, Donald L, 2001. "The Relative Forecasting Performance of the Divisia and Simple Sum Monetary Aggregates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(2), pages 272-283, May.
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    17. Barnett, William & Chauvet, Marcelle & Leiva-Leon, Danilo & Su, Liting, 2016. "Nowcasting Nominal GDP with the Credit-Card Augmented Divisia Monetary," Studies in Applied Economics 59, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    18. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    19. Ellington, Michael, 2018. "The case for Divisia monetary statistics: A Bayesian time-varying approach," Journal of Economic Dynamics and Control, Elsevier, vol. 96(C), pages 26-41.
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    More about this item

    Keywords

    Divisia; Credit-Card-Augmented Divisia; Monetary Aggregates; Forecasting; Bayesian vector autoregression; Inflation; Output Growth.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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