Vector autoregressions: forecasting and reality
AbstractConstructing forecasts of the future path for economic series such as real gross domestic product growth, inflation, and unemployment forms a large part of applied economic analysis for business and government. Model-based forecasts are easier to replicate and validate by independent researchers than forecasts based on expert opinion alone. In addition, the forecaster can formally investigate the source of systematic errors in model forecasts, and a forecast model s performance can be established before it is used by a decision maker. ; The authors of this article describe a particular model-based forecasting approach, a vector autoregression comprising six U.S. macroeconomic variables. They focus attention on the technical hurdles that must be addressed in a real-time application and methods for overcoming those hurdles, such as conditional forecasting to handle the staggered release of data and matching quarterly with monthly data. ; By emphasizing the practical problems of forecasting economic data using a statistical model, the authors draw on experience in using such a model at the Federal Reserve Bank of Atlanta. Although the model studied is small and highly aggregated, it provides a convenient framework for illustrating several practical forecasting issues. The focus on a simple model provides potential users with a road map of how one might implement such a forecasting model in specific applications.
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 Atlanta in its journal Economic Review.
Volume (Year): (1999)
Issue (Month): Q1 ()
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Richard G. Anderson & Kenneth A. Kavajecz, 1994. "A historical perspective on the Federal Reserve's monetary aggregates: definition, construction and targeting," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 1-31.
- Christopher A. Sims, 1989.
"A nine variable probabilistic macroeconomic forecasting model,"
Discussion Paper / Institute for Empirical Macroeconomics
14, Federal Reserve Bank of Minneapolis.
- Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
- Christopher A. Sims, 1992. "A Nine Variable Probabilistic Macroeconomic Forecasting Model," Cowles Foundation Discussion Papers 1034, Cowles Foundation for Research in Economics, Yale University.
- Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983.
"Forecasting and Conditional Projection Using Realistic Prior Distributions,"
NBER Working Papers
1202, National Bureau of Economic Research, Inc.
- Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
- Richard G. Anderson & Kenneth A. Kavajecz, 1994. "A historical perspective on the Federal Reserve's monetary aggregates: definition, construction and targeting," Proceedings, Federal Reserve Bank of St. Louis, issue Mar, pages 1-31.
- John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, issue Q 4, pages 4-20.
- Chow, Gregory C & Lin, An-loh, 1971.
"Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series,"
The Review of Economics and Statistics,
MIT Press, vol. 53(4), pages 372-75, November.
- Tom Doan, . "DISAGGREGATE: RATS procedure to implement general disaggregation (interpolation/distribution) procedure," Statistical Software Components RTS00050, Boston College Department of Economics.
- Tom Doan, . "CHOWLIN: RATS procedure to distribute a series to a higher frequency using related series," Statistical Software Components RTS00036, Boston College Department of Economics.
- Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Meredith Rector).
If references are entirely missing, you can add them using this form.