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Are German National Accounts informationally efficient?

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  • Roland Döhrn

    (RWI - Leibniz Institute for Economic Research)

Abstract

National accounts are subject to major revisions. To improve the reliability of first release data, it is important to know whether subsequent revisions show systematic patterns. Or, in other words, whether national accounts are informationally efficient in the sense that all available information is incorporated into the data. This paper used annual data to test three dimensions of informational efficiency: weak efficiency, strong efficiency, and Nordhaus efficiency. The weak efficiency tests found GDP revisions to be noise, whereas revisions of several GDP components showed systematic patterns. Strong efficiency tests found covariations of GDP revisions with some indicators. Business survey results in particular have the potential to reduce the extent of revisions. Finally, Nordhaus efficiency tests found some indication of revision stickiness.

Suggested Citation

  • Roland Döhrn, 2023. "Are German National Accounts informationally efficient?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 23-42, March.
  • Handle: RePEc:spr:jbuscr:v:19:y:2023:i:1:d:10.1007_s41549-022-00080-y
    DOI: 10.1007/s41549-022-00080-y
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    References listed on IDEAS

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    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 22(Fall), pages 3-12.
    3. Anthony Garratt & Gary Koop & ShaunP. Vahey, 2008. "Forecasting Substantial Data Revisions in the Presence of Model Uncertainty," Economic Journal, Royal Economic Society, vol. 118(530), pages 1128-1144, July.
    4. Strohsal, Till & Wolf, Elias, 2020. "Data revisions to German national accounts: Are initial releases good nowcasts?," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1252-1259.
    5. Jan Jacobs & Jan-Egbert Sturm, 2005. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 93-114, Springer.
    6. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1.
    7. Katharina Glass, 2018. "Predictability of Euro Area Revisions," Macroeconomics and Finance Series 201801, University of Hamburg, Department of Socioeconomics.
    8. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
    9. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    10. Lars-Erik Öller & Karl-Gustav Hansson, 2005. "Revision of National Accounts: Swedish Expenditure Accounts and GDP," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(3), pages 363-385.
    11. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-127, June.
    12. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    13. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
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    More about this item

    Keywords

    National account; Data revision; Informational efficiency;
    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
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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