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Prognose mit nichtparametrischen Verfahren

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

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.

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Paper provided by Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) in its series Papers with number 2004,07.

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Date of creation: 2004
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Handle: RePEc:zbw:caseps:200407
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  1. Härdle, Wolfgang & Herwartz, Helmut & Spokoiny, Vladimir G., 2001. "Time inhomogeneous multiple volatility modelling," SFB 373 Discussion Papers 2001,7, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  2. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  3. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
  4. Francis X. Diebold & James M. Nason, 1989. "Nonparametric exchange rate prediction?," Finance and Economics Discussion Series 81, Board of Governors of the Federal Reserve System (U.S.).
  5. Michael W. Brandt & Francis X. Diebold, 2003. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," NBER Working Papers 9664, National Bureau of Economic Research, Inc.
  6. Wolfgang HÄRDLE & H. LÜTKEPOHL & R. CHEN, 1996. "A Review of Nonparametric Time Series Analysis," SFB 373 Discussion Papers 1996,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  7. Härdle, Wolfgang & Tschernig, Rolf, 2000. "Flexible time series analysis," SFB 373 Discussion Papers 2000,51, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  8. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters, in: Credit and State Theories of Money, chapter 1 Edward Elgar.
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