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Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area

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  • Oliver Hülsewig
  • Johannes Mayr
  • Stéphane Sorbe

Abstract

This paper evaluates short-term forecasts of real GDP in the Euro area derived from the CESifo Economic Climate indicator (WES) in terms of forecast accuracy. We compare the forecast properties of the WES with those of monthly composite indicators. Considering the WES is interesting because (i) it is exclusively based on the assessment of economic experts about the current economic situation, and (ii) it is timely released within the quarter on a quarterly basis. The empirical analysis is carried out under full information, which means that the competing monthly indicators are known for the entire quarter, and under incomplete information. Our findings exhibit that the forecast power of the WES is comparatively proper.

Suggested Citation

  • Oliver Hülsewig & Johannes Mayr & Stéphane Sorbe, 2007. "Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area," ifo Working Paper Series 46, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    • Handle: RePEc:ces:ifowps:_46
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    Cited by:

    1. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    2. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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