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Examining the quality of early GDP component estimates

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  • Sinclair, Tara M.
  • Stekler, H.O.

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, and 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, under most circumstances, despite the existence of some bias, an analyst could use the early data to obtain a realistic picture of what had happened in the economy in the previous quarter.

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  • 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.
    • Handle: RePEc:eee:intfor:v:29:y:2013:i:4:p:736-750
    DOI: 10.1016/j.ijforecast.2012.02.007
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    Cited by:

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    2. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    3. Ducoudré, Bruno & Hubert, Paul & Tabarly, Guilhem, 2020. "The state-dependence of output revisions," Economics Letters, Elsevier, vol. 192(C).
    4. Martinez, Andrew & Schibuola, Alex, 2021. "The Expectations Gap: An Alternative Measure of Economic Slack," Working Papers 11284, George Mason University, Mercatus Center.
    5. 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.
    6. 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.
    7. 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.
    8. repec:hal:spmain:info:hdl:2441/2q9catktmn91sabau2l9qji1as is not listed on IDEAS
    9. Ignacio Martínez, 2021. "Deepening GDP revision analysis: GDP bias breakdown and compositional change," Economic Statistics Series 136, Central Bank of Chile.
    10. repec:amu:wpaper:2013-04 is not listed on IDEAS
    11. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    12. 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.
    13. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    14. 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.
    15. Olga Isengildina‐Massa & Berna Karali & Todd H. Kuethe & Ani L. Katchova, 2021. "Joint Evaluation of the System of USDA's Farm Income Forecasts," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(3), pages 1140-1160, September.
    16. 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.
    17. 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.
    18. Danae Scherman Teitelboim, 2020. "Revisiones en cuentas nacionales trimestrales Chile 2006-2019," Economic Statistics Series 131, Central Bank of Chile.
    19. 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.
    20. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.
    21. 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.
    22. repec:spo:wpmain:info:hdl:2441/4bhjotvnvo9308hhu8rqo497o9 is not listed on IDEAS
    23. Funashima, Yoshito & Iizuka, Nobuo & Ohtsuka, Yoshihiro, 2020. "GDP announcements and stock prices," Journal of Economics and Business, Elsevier, vol. 108(C).
    24. repec:hal:spmain:info:hdl:2441/4bhjotvnvo9308hhu8rqo497o9 is not listed on IDEAS
    25. 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; Mahalanobis distance; 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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