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Examining the Quality of Early GDP Component Estimates

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  • Tara M. Sinclair

    (George Washington University)

  • H.O. Stekler

    (George Washington University)

Abstract

In this paper we examine the quality of the initial estimates of headline GDP and 10 major components of both real and nominal U.S. GDP. We ask a number of questions about various characteristics of the differences between the initial estimates available one month after the end of the quarter to the estimates available three months after the end of the quarter. Do the first estimates have the same directional signs as the later numbers? Are the original numbers unbiased estimates of the later figures? Are any observed biases related to the state of the economy? Finally, we determine whether there is a significant difference between the vector of the 30 day estimates of the 10 major components and the vector of the 90 day estimates of the same components. We conclude that, despite the existence of some bias, under most circumstances, an analyst could use the early data to obtain a realistic picture of what had happened in the economy in the previous quarter.

Suggested Citation

  • Tara M. Sinclair & H.O. Stekler, 2011. "Examining the Quality of Early GDP Component Estimates," Working Papers 2011-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Dec 2011.
  • Handle: RePEc:gwc:wpaper:2011-001
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    References listed on IDEAS

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    Cited by:

    1. Ducoudré, Bruno & Hubert, Paul & Tabarly, Guilhem, 2020. "The state-dependence of output revisions," Economics Letters, Elsevier, vol. 192(C).
    2. Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
    3. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    4. Sinclair, Tara M. & Stekler, H.O. & Carnow, Warren, 2015. "Evaluating a vector of the Fed’s forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 157-164.
    5. 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.
    6. Hans Christian Müller-Dröge & Tara M. Sinclair & H.O. Stekler, 2014. "Evaluating Forecasts of a Vector of Variables: a German Forecasting Competition," CAMA Working Papers 2014-55, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Danae Scherman Teitelboim, 2020. "Revisiones en cuentas nacionales trimestrales Chile 2006-2019," Economic Statistics Series 131, Central Bank of Chile.
    8. Peter A.G. van Bergeijk, 2017. "Making Data Measurement Errors Transparent: The Case of the IMF," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 18(3), pages 133-154, July.
    9. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    10. repec:amu:wpaper:2013-04 is not listed on IDEAS
    11. Lixiong Yang, 2020. "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, vol. 59(6), pages 2663-2687, December.
    12. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.
    13. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    14. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2016. "Does joint modelling of the world economy pay off? Evaluating global forecasts from a Bayesian GVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 86-100.
    15. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    16. Funashima, Yoshito & Iizuka, Nobuo & Ohtsuka, Yoshihiro, 2020. "GDP announcements and stock prices," Journal of Economics and Business, Elsevier, vol. 108(C).
    17. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    18. Eugen Scarlat, 2016. "Connectivity - Based Clustering of GDP Time Series," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 23-38, March.

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    More about this item

    Keywords

    Flash Estimates; Data Revisions; GDP Components; Statistical Tests; Business Cycles;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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