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Prognose mit nichtparametrischen Verfahren

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  • Härdle, Wolfgang Karl
  • Chen, Ying
  • Schulz, Rainer

Abstract

Statistische Prognosen basieren auf der Annahme, dass ein funktionaler Zusammenhang zwischen der zu prognostizierenden Variable y und anderen dimensionalen beobachtbaren Variablen x=(x1,...,xj)t – Rj besteht. Kann der funktionale Zusammenhang geschätzt werden, so kann im Prinzip für jedes x der zugehörige y Wert prognostiziert werden. Bei den meisten Anwendungen wird angenommen, dass der funktionale Zusammenhang einem niedrigdimensionalen parametrischen Modell entspricht oder durch dieses zumindest gut wiedergegeben wird. Ein Beispiel im bivariaten Fall ist das lineare Modell y=b(0)+b(1)x. Sind die beiden unbekannten Parameter b(0) und b(1) mit Hilfe historischer Daten geschätzt, so lässt sich für jedes gegebene x sofort der zugehörige y Wert prognostizieren. Allerdings besteht hierbei die Gefahr, dass der wirkliche funktionale Zusammenhang nicht dem gewählten Modell entspricht. Dies kann in Folge zu schlechten Prognosen führen.

Suggested Citation

  • Härdle, Wolfgang Karl & Chen, Ying & Schulz, Rainer, 2004. "Prognose mit nichtparametrischen Verfahren," Papers 2004,07, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    • Handle: RePEc:zbw:caseps:200407
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    Cited by:

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    3. Borak, Szymon & Misiorek, Adam & Weron, Rafał, 2010. "Models for heavy-tailed asset returns," SFB 649 Discussion Papers 2010-049, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Panov, Vladimir, 2010. "Estimation of the signal subspace without estimation of the inverse covariance matrix," SFB 649 Discussion Papers 2010-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    6. Schulze, Franziska, 2010. "Spatial dependencies in German matching functions," SFB 649 Discussion Papers 2010-054, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Grith, Maria & Krätschmer, Volker, 2010. "Parametric estimation of risk neutral density functions," SFB 649 Discussion Papers 2010-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    10. Janek, Agnieszka & Kluge, Tino & Weron, Rafał & Wystup, Uwe, 2010. "FX smile in the Heston model," SFB 649 Discussion Papers 2010-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Wiebach, Nicole & Hildebrandt, Lutz, 2010. "Context effects as customer reaction on delisting of brands," SFB 649 Discussion Papers 2010-056, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    14. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2014. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 89-121.
    15. Mammen, Enno & Rothe, Christoph & Schienle, Melanie, 2010. "Nonparametric regression with nonparametrically generated covariates," SFB 649 Discussion Papers 2010-059, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    17. Sabiwalsky, Ralf, 2010. "Executive compensation regulation and the dynamics of the pay-performance sensitivity," SFB 649 Discussion Papers 2010-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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