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Forecasting with a real-time data set for macroeconomists

  • Tom Stark
  • Dean Croushore

This paper discusses how forecasts are affected by the use of real-time data rather than latest-available data. The key issue is this: In the literature on developing forecasting models, new models are put together based on the results they yield using the data set available to the model’s developer. But those are not the data that were available to a forecaster in real time. How much difference does the vintage of the data make for such forecasts? The authors explore this issue with a variety of exercises designed to answer this question. In particular, they find that the use of real-time data matters for some forecasting issues but not for others. It matters for choosing lag length in a univariate context. Preliminary evidence suggests that the span—or number—of forecast observations used to evaluate models may also be critical: the authors find that standard measures of forecast accuracy can be vintage-sensitive when constructed on the short spans (five years of quarterly data) of data sometimes used by researchers for forecast evaluation. The differences between using real-time and latest-available data may depend on what is being used as the “actual” or realization, and we explore several alternatives that can be used. Perhaps of most importance, we show that measures of forecast error, such as root-mean-squared error and mean absolute error, can be deceptively lower when using latest-available data rather than real-time data. Thus, for purposes such as modeling expectations or evaluating forecast errors of survey data, the use of latest-available data is questionable; comparisons between the forecasts generated from new models and benchmark forecasts, generated in real time, should be based on real-time data.

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File URL: http://www.philadelphiafed.org/research-and-data/publications/working-papers/2001/wp01-10.pdf
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Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 01-10.

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Date of creation: 2001
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Handle: RePEc:fip:fedpwp:01-10
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  1. Evan F. Koenig & Sheila Dolmas, 1997. "Real-time GDP Growth Forecasts," Working Papers 9710, Federal Reserve Bank of Dallas.
  2. Norman R. Swanson & Halbert White, 1995. "A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Macroeconomics 9503004, EconWPA.
  3. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
  4. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
  5. Tom Stark & Dean Croushore, 2001. "Forecasting with a real-time data set for macroeconomists," Working Papers 01-10, Federal Reserve Bank of Philadelphia.
  6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  7. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
  8. Rosanne Cole, 1969. "Data Errors and Forecasting Accuracy," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 47-82 National Bureau of Economic Research, Inc.
  9. Stephen K. McNees, 1992. "How large are economic forecast errors?," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 25-42.
  10. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
  11. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers 4-96-7, Pennsylvania State - Department of Economics.
  12. Dean Croushore & Tom Stark, 1999. "Does data vintage matter for forecasting?," Working Papers 99-15, Federal Reserve Bank of Philadelphia.
  13. 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.
  14. Dean Croushore & Tom Stark, 1999. "A real-time data set for marcoeconomists: does the data vintage matter?," Working Papers 99-21, Federal Reserve Bank of Philadelphia.
  15. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
  16. 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.
  17. Tom Stark, 1998. "A Bayesian vector error corrections model of the U.S. economy," Working Papers 98-12, Federal Reserve Bank of Philadelphia.
  18. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  19. Trivellato, Ugo & Rettore, Enrico, 1986. "Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 445-53, October.
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