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A real-time data set for macroeconomists: does data vintage matter for forecasting?

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  • Dean Croushore
  • Tom Stark

Abstract

This paper describes a real-time data set for macroeconomists that can be used for a variety of purposes, including forecast evaluation. The data set consists of quarterly vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, and provides an example showing how data revisions can affect forecasts.

Suggested Citation

  • Dean Croushore & Tom Stark, 2000. "A real-time data set for macroeconomists: does data vintage matter for forecasting?," Working Papers 00-6, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:00-6
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    References listed on IDEAS

    as
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    Cited by:

    1. Karen E. Dynan & Douglas W. Elmendorf, 2001. "Do provisional estimates of output miss economic turning points?," Finance and Economics Discussion Series 2001-52, Board of Governors of the Federal Reserve System (U.S.).
    2. Hui Feng, 2009. "Real-time or current vintage: does the type of data matter for forecasting and model selection?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 183-193.
    3. Fackler, James S., 2002. "Comment on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 559-562, December.
    4. Scott Schuh, 2001. "An evaluation of recent macroeconomic forecast errors," New England Economic Review, Federal Reserve Bank of Boston, pages 35-56.
    5. William B. English & William R. Nelson & Brian P. Sack, 2002. "Interpreting the significance of lagged interest rate in estimated monetary policy rules," Finance and Economics Discussion Series 2002-24, Board of Governors of the Federal Reserve System (U.S.).
    6. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 276, European Central Bank.

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