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Konjunkturelle Frühindikatoren in der Krise: weiche Faktoren stärker als harte

Author

Listed:
  • Konstantin A. Kholodilin
  • Stefan Kooths

Abstract

Die aktuelle Wirtschaftskrise wirft die Frage auf, ob nicht durch eine bessere Ausschöpfung der in den verschiedenen Frühindikatoren enthaltenen Informationen die aufgetretenen Prognosefehler hätten vermieden werden können. Dies gilt insbesondere vor dem Hintergrund des überraschend abrupten konjunkturellen Einbruchs. Auf der Basis eines umfangreichen Datensatzes wird mit verschiedenen ökonometrischen Verfahren nach einem Frühindikatorensystem gesucht, das aussagekräftiger ist. Neben dem Test alternativer ökonometrischer Ansätze gehen wir dabei auch der zentralen Frage nach: Sind weiche, also umfragebasierte Indikatoren, die die Erwartungen der wirtschaftlichen Entscheidungsträger abfragen, in Zeiten heftiger konjunktureller Verwirbelungen zuverlässiger als harte Indikatoren, die erst nachträglich die Ergebnisse ökonomischer Entscheidungen beschreiben.

Suggested Citation

  • Konstantin A. Kholodilin & Stefan Kooths, 2009. "Konjunkturelle Frühindikatoren in der Krise: weiche Faktoren stärker als harte," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 76(21), pages 348-354.
  • Handle: RePEc:diw:diwwob:76-21-3
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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.98246.de/09-21-3.pdf
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    References listed on IDEAS

    as
    1. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
    2. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Mixed data sampling; Nowcasting; Factor forecasts; German business conditions;

    JEL classification:

    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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