Using monthly data to improve quarterly model forecasts
AbstractThis 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.
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Bibliographic InfoArticle provided by Federal Reserve Bank of Minneapolis in its journal Quarterly Review.
Volume (Year): (1996)
Issue (Month): Spr ()
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- 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.
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- 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.
- Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
- Hukkinen, Juhana & Viren, Matti, 1998. "How to Evaluate the Forecasting Performance of a Macroeconomic Model," Research Discussion Papers 5/1998, Bank of Finland.
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