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The real-time predictive content of money for output

Author

Listed:
  • Norman R. Swanson

    (Rutgers University - Department of Economics)

  • Jeffery D. Amato

    (Goldman Sachs International)

Abstract

Data on monetary aggregates are subject to periodic redefinitions, presumably in part to improve their link to measures of output. Money data are also revised on a regular basis. Taking these data imperfections into account, we reassess the evidence on the marginal predictive content of M1 and m2 for real and nominal output. In particular, by first using the latest version of the data that is available, and then using sequences of historical time series that would have been available to forecasters in real-time, we are able to provide a comprehensive assessment of whether money is useful for predicting output. We conclude that the generally significant marginal predictive content of M1 and m2 for output that is found using a recently revised data set is not duplicated in a real-time setting, although M2 is shown to remain useful when 1-year ahead forecasts are constructed using fitted vector autoregressive models.

Suggested Citation

  • Norman R. Swanson & Jeffery D. Amato, 2000. "The real-time predictive content of money for output," BIS Working Papers 96, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:96
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    Cited by:

    1. Dean Croushore & Tom Stark, 2002. "Is macroeconomic research robust to alternative data sets?," Working Papers 02-3, Federal Reserve Bank of Philadelphia.
    2. Tracy Chan & Ramdane Djoudad & Jackson Loi, 2006. "Regime Shifts in the Indicator Properties of Narrow Money in Canada," Staff Working Papers 06-6, Bank of Canada.
    3. Jonathan B. Hill, 2007. "Efficient tests of long-run causation in trivariate VAR processes with a rolling window study of the money-income relationship," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 747-765.
    4. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    5. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    6. Fiordelisi, Franco & Marques-Ibanez, David & Molyneux, Phil, 2011. "Efficiency and risk in European banking," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1315-1326, May.
    7. Yash P. Mehra, 2002. "Survey measures of expected inflation : revisiting the issues of predictive content and rationality," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 17-36.
    8. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    9. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, EconWPA.
    10. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2008. "Real-Time Representations of the Output Gap," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 792-804, November.
    11. Joao Sousa & Andrea Zaghini, 2008. "Monetary policy shocks in the euro area and global liquidity spillovers," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 13(3), pages 205-218.
    12. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    13. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    14. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    15. Mita Bhattacharya & Paresh Narayan & Stephan Popp & Badri Rath, 2011. "The productivity-wage and productivity-employment nexus: a panel data analysis of Indian manufacturing," Empirical Economics, Springer, vol. 40(2), pages 285-303, April.
    16. Claus Brand & Hans-Eggert Reimers & Franz Seitz, 2003. "Narrow Money and the Business Cycle: Theoretical aspects and euro area evdence," Macroeconomics 0303012, EconWPA.
    17. Fabrice Collard & Harris Dellas, 2010. "Monetary Misperceptions, Output, and Inflation Dynamics," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(2-3), pages 483-502, March.
    18. Pons Novell, J., 2002. "Ciclo de la economía española y contenido informativo de los tipos de interés," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 20, pages 583-598, Diciembre.
    19. Joe Haslag & R.W. Hafer & Garett Jones, 2003. "The Effect of Monetary Policy on Economic Output," Working Papers 0311, Department of Economics, University of Missouri.
    20. Scharnagl, Michael & Gerberding, Christina & Seitz, Franz, 2007. "Simple interest rate rules with a role for money," Discussion Paper Series 1: Economic Studies 2007,31, Deutsche Bundesbank.
    21. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representation of the UK Output Gap in the Presence of Trend Uncertainty," Birkbeck Working Papers in Economics and Finance 0618, Birkbeck, Department of Economics, Mathematics & Statistics.
    22. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    23. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.

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