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The use and abuse of \"real-time\" data in economic forecasting

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Listed:
  • Sheila Dolmas
  • Evan F. Koenig
  • Jeremy M. Piger

Abstract

We distinguish between three different ways of using real-time data to estimate forecasting equations and argue that the most frequently used approach should generally be avoided. The point is illustrated with a model that uses monthly observations of industrial production, employment, and retail sales to predict real GDP growth. When the model is estimated using our preferred method, its out-of-sample forecasting performance is clearly superior to that obtained using conventional estimation, and compares favorably with that of the Blue-Chip consensus.

Suggested Citation

  • Sheila Dolmas & Evan F. Koenig & Jeremy M. Piger, 2000. "The use and abuse of \"real-time\" data in economic forecasting," Working Papers 0004, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddwp:00-04
    Note: Published as: Koenig, Evan F., Shelia Dolmas and Jeremy Piger (2003), "The Use and Abuse of "Real-Time" Data in Economic Forecasting," The Review of Economics and Statistics 85 (3): 618-628.
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    References listed on IDEAS

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    1. Stephen K. McNees, 1987. "Consensus forecasts: tyranny of the majority?," New England Economic Review, Federal Reserve Bank of Boston, issue Nov, pages 15-21.
    2. Orphanides, Athanasios, 2003. "The quest for prosperity without inflation," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 633-663, April.
    3. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    4. Daniel M. Chin & Preston J. Miller, 1996. "Using monthly data to improve quarterly model forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 20(Spr), pages 16-33.
    5. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
    6. 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.
    7. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
    8. 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.
    9. 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.
    10. 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.
    11. Evan F. Koenig, 1996. "Capacity utilization as a real-time predictor of manufacturing output," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q III, pages 16-23.
    12. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    13. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 22(Fall), pages 3-12.
    14. Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
    15. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    16. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 293-318.
    17. Bharat Trehan, 1989. "Forecasting growth in current quarter real GNP," Economic Review, Federal Reserve Bank of San Francisco, issue Win, pages 39-52.
    18. Braun, Steven N, 1990. "Estimation of Current-Quarter Gross National Product by Pooling Preliminary Labor-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 293-304, July.
    19. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    20. Tilman Ehrbeck & Robert Waldmann, 1996. "Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(1), pages 21-40.
    21. Terry J. Fitzgerald & Preston J. Miller, 1989. "A simple way to estimate current-quarter GNP," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 13(Fall), pages 27-31.
    22. R. Mark Rogers, 1998. "A primer on short-term linkages between key economic data series," Economic Review, Federal Reserve Bank of Atlanta, vol. 83(Q 2), pages 40-54.
    23. 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.).
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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