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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, Research Program on Forecasting, revised Dec 2011.
    • Handle: RePEc:gwc:wpaper:2011-001
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    File URL: https://www2.gwu.edu/~forcpgm/2011-001.pdf
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. repec:amu:wpaper:2013-04 is not listed on IDEAS
    7. 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.
    8. Tara M. Sinclair, 2012. "Characteristics and Implications of Chinese Macroeconomic Data Revisions," Working Papers 2012-09, The George Washington University, Institute for International Economic Policy.

    More about this item

    Keywords

    Flash Estimates; Data Revisions; GDP Components; Statistical Tests; Business Cycles;

    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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