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Data vintages and measuring forecast model performance

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  • John C. Robertson
  • Ellis W. Tallman

Abstract

The data on economic variables are usually estimates, and these estimates may be revised many times after their initial publication. Most historical forecast evaluation exercises use the \\"latest available\\" or most recently revised vintage of historical data when constructing the forecasts-that is, they use estimates that may well have been unavailable to a forecaster in real time. Evaluations using such data could thus give a misleading picture of the forecast performance that can be expected in real-time situations. This fact is particularly relevant if a forecasting model's performance is to be compared with that of published real-time forecasts. One practical question is whether actually using the data set available to a forecaster in real time would lead to inferences that are substantially different from those made using the latest available vintage of data. A related question is whether it matters which vintage of data the forecasts are evaluated against. ; The authors argue that the choice of data vintage can have both a quantitative and a qualitative influence on forecast and model comparisons, at least over short horizons. This influence is illustrated by examining the performance of the composite index of leading indicators as a forecaster of alternative measures of real output. However, more research is required in order to determine whether the results generalize to forecasts of other series that are subject to revision, such as the various money aggregate measures.

Suggested Citation

  • John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, vol. 83(Q 4), pages 4-20.
  • Handle: RePEc:fip:fedaer:y:1998:i:q4:p:4-20:n:v.83no.4
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    References listed on IDEAS

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    1. Swanson Norman, 1996. "Forecasting Using First-Available Versus Fully Revised Economic Time-Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(1), pages 1-20, April.
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    3. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    4. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    5. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    6. Stephen K. McNees, 1988. "How accurate are macroeconomic forecasts?," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 15-36.
    7. Makridakis, Spyros & Chatfield, Chris & Hibon, Michele & Lawrence, Michael & Mills, Terence & Ord, Keith & Simmons, LeRoy F., 1993. "The M2-competition: A real-time judgmentally based forecasting study," International Journal of Forecasting, Elsevier, vol. 9(1), pages 5-22, April.
    8. 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.).
    9. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
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