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How to treat benchmark revisions? The case of German production and orders statistics

Author

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  • Knetsch, Thomas A.
  • Reimers, Hans-Eggert

Abstract

Elements of an econometric examination of benchmark revisions in real-time data are suggested. Structural break tests may be applied to detect heterogeneities within vintages. Systems cointegration tests are helpful to reveal inconsistencies across vintages. Differencing and rebasing, often used to adjust for benchmark revisions, are generally not sufficient to ensure consistent real-time macroeconomic data. Vintage transformation functions estimated by cointegrating regressions are more flexible. Inappropriate conversion may cause observed revision statistics to be affected by nuisance parameters. In German industrial production and orders statistics, remaining revisions are generally biased and serially correlated.

Suggested Citation

  • Knetsch, Thomas A. & Reimers, Hans-Eggert, 2006. "How to treat benchmark revisions? The case of German production and orders statistics," Discussion Paper Series 1: Economic Studies 2006,38, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdp1:5155
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    References listed on IDEAS

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    1. Søren Johansen & Rocco Mosconi & Bent Nielsen, 2000. "Cointegration analysis in the presence of structural breaks in the deterministic trend," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 216-249.
    2. Swanson Norman, 1996. "Forecasting Using First-Available Versus Fully Revised Economic Time-Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(1), pages 1-20, April.
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    Cited by:

    1. Thomas A. Knetsch & Hans‐Eggert Reimers, 2009. "Dealing with Benchmark Revisions in Real‐Time Data: The Case of German Production and Orders Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(2), pages 209-235, April.
    2. Joachim Möller, 2012. "From a Bulwark of Eurosclerosis to a Flexibility Champion? Why Did the German Economy and the Labour Market Do So Well During and After the Recession?," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(02), pages 14-19, August.
    3. Jan Jacobs & Jan-Egbert Sturm, 2009. "The information content of KOF indicators on Swiss current account data revisions," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(2), pages 161-181.
    4. Boysen-Hogrefe, Jens & Neuwirth, Stefan, 2012. "The impact of seasonal and price adjustments on the predictability of German GDP revisions," Kiel Working Papers 1753, Kiel Institute for the World Economy.
    5. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do forecasters inform or reassure?," KOF Working papers 09-215, KOF Swiss Economic Institute, ETH Zurich.
    6. Joachim Möller, 2012. "From a Bulwark of Eurosclerosis to a Flexibility Champion? Why Did the German Economy and the Labour Market Do So Well During and After the Recession?," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(2), pages 14-19, 08.
    7. repec:ces:ifodic:v:10:y:2012:i:2:p:18947996 is not listed on IDEAS

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    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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