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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Federal Reserve Bank of Minneapolis in its journal Quarterly Review.
Volume (Year): (1996)
Issue (Month): Spr ()
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Evan Koenig & Sheila Dolmas & Jeremy M. Piger, 2002.
"The use and abuse of 'real-time' data in economic forecasting,"
2001-015, Federal Reserve Bank of St. Louis.
- 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.
- Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," Working Papers 0004, Federal Reserve Bank of Dallas.
- Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- 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.
- John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
- 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.
- Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
- 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.
- Hukkinen, Juhana & Viren, Matti, 1998. "How to Evaluate the Forecasting Performance of a Macroeconomic Model," Research Discussion Papers 5/1998, Bank of Finland.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Janelle Ruswick).
If references are entirely missing, you can add them using this form.