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Using monthly data to improve quarterly model forecasts


  • Preston J. Miller
  • Daniel M. Chin


This article describes a new way to use monthly data to improve the national forecasts of quarterly economic models. This new method combines the forecasts of a monthly model with those of a quarterly model using weights that maximize forecasting accuracy. While none of the method's steps is new, it is the first method to include all of them. It is also the first method to be shown to improve quarterly model forecasts in a statistically significant way. And it is the first systematic forecasting method to be shown, statistically, to forecast as well as the popular survey of major economic forecasters published in the Blue Chip Economic Indicators newsletter. The method was designed for use with the quarterly model maintained in the Research Department of the Minneapolis Federal Reserve Bank, but can be tailored to fit other models. The Minneapolis Fed model is a Bayesian-restricted vector autoregression model.

Suggested Citation

  • Preston J. Miller & Daniel M. Chin, 1996. "Using monthly data to improve quarterly model forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr, pages 16-33.
  • Handle: RePEc:fip:fedmqr:y:1996:i:spr:p:16-33:n:v.20no.2

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    References listed on IDEAS

    1. Paul W. Bauer & Diana Hancock, 1995. "Scale economies and technological change in Federal Reserve ACH payment processing," Economic Review, Federal Reserve Bank of Cleveland, issue Q III, pages 14-29.
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    Cited by:

    1. Zadrozny, Peter A., 2016. "Extended Yule–Walker identification of VARMA models with single- or mixed-frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 438-446.
    2. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
    3. repec:taf:applec:v:49:y:2017:i:38:p:3880-3890 is not listed on IDEAS
    4. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    5. Alain MAURIN & Alain GUAY, "undated". "An Adaptation of the MIDAS Regression Model for Estimating and Forecasting Quarterly GDP : Application to the Case of Guadeloupe," EcoMod2008 23800085, EcoMod.
    6. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
    7. Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
    8. Hukkinen, Juhana & Viren, Matti, 1999. "Assessing the Forecasting Performance of a Macroeconomic Model," Journal of Policy Modeling, Elsevier, vol. 21(6), pages 753-768, November.
    9. William T. Gavin & Kevin L. Kliesen, 2002. "Unemployment insurance claims and economic activity," Review, Federal Reserve Bank of St. Louis, issue May, pages 15-28.
    10. Rómulo Chumacero & Jorge Quiroz, 1996. "La Tasa Natural de Crecimiento de la Economía Chilena: 1985-1996," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 33(100), pages 453-472.
    11. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.

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