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

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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. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2006,38.

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Date of creation: 2006
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Handle: RePEc:zbw:bubdp1:5155

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Keywords: real-time data; benchmark revisions; industrial production; orders;

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  1. Falko Fecht & Kevin X.D. Huang & Antoine Martin, 2007. "Financial Intermediaries, Markets, and Growth," Vanderbilt University Department of Economics Working Papers 0714, Vanderbilt University Department of Economics.
  2. Koetter, M. & Bos, J.W.B. & Heid, F. & Kolari, J.W. & Kool, C.J.M. & Porath, D., 2007. "Accounting for distress in bank mergers," Journal of Banking & Finance, Elsevier, Elsevier, vol. 31(10), pages 3200-3217, October.
  3. Campbell, John Y. & Hilscher, Jens & Szilagyi, Jan, 2005. "In search of distress risk," Discussion Paper Series 1: Economic Studies 2005,27, Deutsche Bundesbank, Research Centre.
  4. Bent Nielsen & Soren Johansen and Rocco Mosconi, 2000. "Cointegration analysis in the presence of structural breaks in the deterministic trend," Economics Series Working Papers 2000-W22, University of Oxford, Department of Economics.
  5. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers, Pennsylvania State - Department of Economics 4-96-7, Pennsylvania State - Department of Economics.
  6. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2007. "Learning, Structural Instability, and Present Value Calculations," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 26(2-4), pages 253-288.
  7. von Kalckreuth, Ulf, 2005. "A "wreckers theory" of financial distress," Discussion Paper Series 1: Economic Studies 2005,40, Deutsche Bundesbank, Research Centre.
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Cited by:
  1. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do Forecasters Inform or Reassure?: Evaluation of the German Real-Time Data," Discussion Papers of DIW Berlin 858, DIW Berlin, German Institute for Economic Research.
  2. Jens Boysen-Hogrefe & Stefan Neuwirth, 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.
  3. Jan Jacobs & Jan-Egbert Sturm, 2008. "The information content of KOF indicators on Swiss current account data revisions," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2008(2), pages 161-181.
  4. 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?," CESifo DICE Report, Ifo Institute for Economic Research at the University of Munich, Ifo Institute for Economic Research at the University of Munich, vol. 10(2), pages 14-19, 08.

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