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Forecasting chaotic systems : the role of local Lyapunov exponents

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Author Info
Dominique Guegan () (Centre d'Economie de la Sorbonne et Paris School of Economics)
Justin Leroux () (HEC Montréal and CIRPEE)

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Abstract

We propose a novel methodology for forecasting chaotic systems which is based on the nearest-neighbor predictor and improves upon it by incorporating local Lyapunov exponents to correct for its inevitable bias. Using simulated data, we show that gains in prediction accuracy can be substantial. The general intuition behind to proposed method can readily be applied to other non-parametric predictors.

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2008/B08014.pdf
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Publisher Info
Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number b08014.

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Length: 9 pages
Date of creation: Feb 2008
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Handle: RePEc:mse:cesdoc:b08014

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Web page: http://ces.univ-paris1.fr/
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Related research
Keywords: Chaos theory; Lyapunov exponent; logistic map; Monte Carlo simulations.;

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Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C65 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Miscellaneous Mathematical Tools

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  1. Dominique Guégan & Justin Leroux, 2008. "Local Lyapunov exponents: Zero plays no role in Forecasting chaotic systems," Cahiers de recherche 08-10, HEC Montréal, Institut d'économie appliquée. [Downloadable!]
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