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Forecasting with a Real-Time Data Set for Macroeconomists

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Author Info
Tom Stark and Dean Croushore

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Abstract

This paper discusses how forecasts may be 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 developer. But those aren't the data that were available to a forecaster in real time. How much difference does the vintage of the data make for such forecasts? We explore this issue with a variety of exercises designed to answer this question. In particular, we find that real-time data matters for some forecasting issues but not for others. It matters for choosing lag length in a univariate context. It may matter considerably for a short-horizon forecast, though is less important for longer-horizon forecasts. Preliminary evidence suggests that the span--or number--of forecast observations used to evaluate models may also be critical: we find that standard measures of forecast accuracy can be vintage-sensitive when constructed on the short spans (5 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's 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, developing a model using latest-available data is questionable; model development may be much better if it's based on real-time data.

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Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 258.

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Date of creation: 01 Apr 2001
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Handle: RePEc:sce:scecf1:258

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Related research
Keywords: Forecasting; Real-time data; Macroeconomics;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

References listed on IDEAS
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  1. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis. [Downloadable!]
    Other versions:
  2. 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.).
  3. 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, 07. [Downloadable!] (restricted)
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  4. 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. [Downloadable!] (restricted)
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  5. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, 04. [Downloadable!] (restricted)
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  6. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers 4-96-7, Pennsylvania State - Department of Economics.
  7. 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. [Downloadable!]
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  10. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia. [Downloadable!]
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  11. Stephen K. McNees, 1992. "How large are economic forecast errors?," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 25-42.
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  14. 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. [Downloadable!]
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    Other versions:
  16. Evan F. Koenig & Sheila Dolmas, 1997. "Real-time GDP Growth Forecasts," Working Papers 97-10, Federal Reserve Bank of Dallas. [Downloadable!]
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Cited by:
(explanations, 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.)

  1. Dean Croushore & Tom Stark, 2002. "Is macroeconomic research robust to alternative data sets?," Working Papers 02-3, Federal Reserve Bank of Philadelphia. [Downloadable!]
  2. Carlos Capistrán-Carmona, 2005. "Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious?," Computing in Economics and Finance 2005 127, Society for Computational Economics. [Downloadable!]
    Other versions:
  3. Pär Österholm & Jeromin Zettelmeyer, 2007. "The Effect of External Conditions on Growth in Latin America," IMF Working Papers 07/176, International Monetary Fund. [Downloadable!]
  4. Tatevik Sekhposyan & Barbara Rossi, 2008. "Has models’ forecasting performance for US output growth and inflation changed over time, and when?," Working Papers 09-02, Duke University, Department of Economics. [Downloadable!]
  5. Dean Croushore, 2006. "An evaluation of inflation forecasts from surveys using real-time data," Working Papers 06-19, Federal Reserve Bank of Philadelphia. [Downloadable!]
  6. Sharon Kozicki, 2001. "Implications of real-time data for forecasting and modeling expectations," Research Working Paper RWP 01-12, Federal Reserve Bank of Kansas City. [Downloadable!]
  7. Masahiro Ashiya, 2005. "Twenty-two years of Japanese institutional forecasts," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(2), pages 79-84, March. [Downloadable!] (restricted)
  8. Tatevik Sekhposyan & Barbara Rossi, 2009. "Has Economic Models’ Forecasting Performance for US Output Growth and Inflation Changed Over Time, and When?," Working Papers 09-06, Duke University, Department of Economics. [Downloadable!]
  9. Maximo Camacho & Gabriel Perez-Quiros, 2009. "Ñ-STING: España Short Term INdicator of Growth," Banco de España Working Papers 0912, Banco de España. [Downloadable!]
  10. Wiliam Branch & George W. Evans, 2005. "A Simple Recursive Forecasting Model," University of Oregon Economics Department Working Papers 2005-3, University of Oregon Economics Department, revised 01 Feb 2005. [Downloadable!]
    Other versions:
  11. Carlo Altavilla & Matteo Ciccarelli, 2007. "Information combination and forecast (st)ability. Evidence from vintages of time-series data," Working Paper Series 846, European Central Bank. [Downloadable!]
  12. Richard G. Anderson, 2006. "Replicability, real-time data, and the science of economic research: FRED, ALFRED, and VDC," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 81-93. [Downloadable!]
  13. Todd E. Clark & Michael W. McCracken, 2008. "Tests of equal predictive ability with real-time data," Working Papers 2008-029, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  14. Dean Croushore, 2008. "Frontiers of real-time data analysis," Working Papers 08-4, Federal Reserve Bank of Philadelphia. [Downloadable!]
  15. Döpke, Jörg, 2004. "Real-time data and business cycle analysis in Germany," Discussion Paper Series 1: Economic Studies 2004,11, Deutsche Bundesbank, Research Centre. [Downloadable!]
  16. Bernhardsen, Tom & Eitrheim, Øyvind & Jore, Anne Sofie & Røisland, Øistein, 2004. "Real-time Data for Norway: Challenges for Monetary Policy," Discussion Paper Series 1: Economic Studies 2004,26, Deutsche Bundesbank, Research Centre. [Downloadable!]
    Other versions:
  17. Tom Bernhardsen & ØYvind Eitrheim, 2005. "Real-time data for Norway: Output gap revisions and challenges for monetary policy," Computing in Economics and Finance 2005 274, Society for Computational Economics. [Downloadable!]
  18. Croushore, Dean, 2004. "Do Consumer Confidence Indexes Help Forecast Consumer Spending in Real Time?," Discussion Paper Series 1: Economic Studies 2004,27, Deutsche Bundesbank, Research Centre. [Downloadable!]
    Other versions:
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