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Do provisional estimates of output miss economic turning points?

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  • Karen E. Dynan
  • Douglas W. Elmendorf

Abstract

Initial estimates of aggregate output and its components are based on very incomplete source data, so they may not fully capture shifts in economic conditions. In particular, if those estimates are based partly on trends in preceding quarters, provisional estimates may overstate activity when actual output is decelerating and understate it when actual output is accelerating. We examine this issue using the Real Time Data Set for Macroeconomists, which contains contemporaneous estimates of GNP or GDP and its components beginning in the late 1960s, as well as financial-market information and other data. We find that provisional estimates tend to partially miss accelerations and decelerations. We also consider whether better use of contemporaneous data could improve the quality of provisional estimates. We find that provisional estimates do not represent optimal forecasts of the current estimates, but that the improvement in forecast quality from including additional data appears to be quite small.

Suggested Citation

  • 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.).
  • Handle: RePEc:fip:fedgfe:2001-52
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    References listed on IDEAS

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    Cited by:

    1. Sinclair, Tara M. & Joutz, Fred & Stekler, H.O., 2010. "Can the Fed predict the state of the economy?," Economics Letters, Elsevier, vol. 108(1), pages 28-32, July.
    2. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    3. Jeremy J. Nalewaik, 2008. "Lack of signal error (LoSE) and implications for OLS regression: measurement error for macro data," Finance and Economics Discussion Series 2008-15, Board of Governors of the Federal Reserve System (U.S.).
    4. Eicher, Theo S. & Kuenzel, David J. & Papageorgiou, Chris & Christofides, Charis, 2019. "Forecasts in times of crises," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1143-1159.
    5. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    6. Vázquez, Jesús & María-Dolores, Ramón & Londoño, Juan M., 2012. "The Effect of Data Revisions on the Basic New Keynesian Model," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 235-249.
    7. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    8. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    9. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    10. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    11. Dennis Fixler & Bruce Grimm, 2006. "GDP Estimates: Rationality Tests and Turning Point Performance," Journal of Productivity Analysis, Springer, vol. 25(3), pages 213-229, June.
    12. Lukas Reiss, 2009. "The Effectiveness of Fiscal Stimulus Packages in Times of Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 1, pages 78-99.
    13. Monica Martin & Cristiano Papile, 2004. "The Bank of Canada's Business Outlook Survey: An Assessment," Staff Working Papers 04-15, Bank of Canada.
    14. Hara, Naoko & Ichiue, Hibiki, 2011. "Real-time analysis on Japan's labor productivity," Journal of the Japanese and International Economies, Elsevier, vol. 25(2), pages 107-130, June.
    15. Kitchen, John & Monaco, Ralph, 2003. "Real-Time Forecasting in Practice: The U.S. Treasury Staff's Real-Time GDP Forecast System," MPRA Paper 21068, University Library of Munich, Germany, revised Oct 2003.
    16. Dennis J. Fixler & Jeremy J. Nalewaik, 2007. "News, noise, and estimates of the \"true\" unobserved state of the economy," Finance and Economics Discussion Series 2007-34, Board of Governors of the Federal Reserve System (U.S.).
    17. Dynan, Karen E. & Elmendorf, Douglas W. & Sichel, Daniel E., 2006. "Can financial innovation help to explain the reduced volatility of economic activity?," Journal of Monetary Economics, Elsevier, vol. 53(1), pages 123-150, January.
    18. Döpke, Jörg, 2004. "Real-time data and business cycle analysis in Germany," Discussion Paper Series 1: Economic Studies 2004,11, Deutsche Bundesbank.
    19. Nalewaik, Jeremy J., 2011. "Incorporating vintage differences and forecasts into Markov switching models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 281-307, April.
    20. Nalewaik, Jeremy J., 2011. "Incorporating vintage differences and forecasts into Markov switching models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 281-307.
    21. Walpurga Köhler-Töglhofer & Lukas Reiss, 2009. "The Effectiveness of Fiscal Stimulus Packages in Times of Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/09, pages 78-99.
    22. Ronel Elul & Joseph M. Silverstein & Tom Stark, 2014. "Understanding house price index revisions," Working Papers 14-38, Federal Reserve Bank of Philadelphia.
    23. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.

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    Forecasting; Macroeconomics;

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