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Analyse der Prognoseeigenschaften von ifo-Konjunkturindikatoren unter Echtzeitbedingungen

  • Gerit Vogt

    ()

    (ifo Institut für Wirtschaftsforschung)

In recent years some paers have been bublished that deal with the forecasting performance of indicators for the German economy. The real-time aspect, however, was largely neglected. This article analyses the information content of some ifo indicators (the business climate index for the manufacturing sector and its components, the current business situation and business expectations) to predict the German index of production. The analysis is based on cross correlations, Granger causality tests and different out-of-sample forecasts, generated by ubset VAR models. First, the out-of-sample forecasts are made, as in conventional studies, with the latest available data and fixed model structure. Afterwards, the out-of-sample indicator properties are analysed in real-time, i.e. with real-time data and variable model structure. In general the indicator properties become worse under real-time conditions. The indicator-based VAR models are not able to beat the forecast performance of a pur autoregressive model for forecast horizons of one and three month. But for forecast horizons of six, nine and twelve months, the indicators seem to be useful in predicting the index of production.

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Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 227 (2007)
Issue (Month): 1 (February)
Pages: 87-101

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Handle: RePEc:jns:jbstat:v:227:y:2007:i:1:p:87-101
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  1. Athanasios Orphanides & Simon van Norden, 1999. "The Reliability of Output Gap Estimates in Real Time," Macroeconomics 9907006, EconWPA.
  2. Jan Jacobs & Jan-Egbert Sturm, 2004. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," CESifo Working Paper Series 1205, CESifo Group Munich.
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  8. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, EconWPA.
  9. Tom Stark and Dean Croushore, 2001. "Forecasting with a Real-Time Data Set for Macroeconomists," Computing in Economics and Finance 2001 258, Society for Computational Economics.
  10. Christian Dreger & Christian Schumacher, 2005. "Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 71-87.
  11. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research.
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