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Kernel Based Nonlinear Canonical Analysis

Author

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  • Serge Darolles

    (Crest)

  • Jean-Pierre Florens

    (Crest)

  • Christian Gourieroux

    (Crest)

Abstract

We consider a kernel based approach to nonlinear canonical correlation analysis and its implementation for time series. We deduce various diagnostics for reversible processes and gaussian processes. The method is first applied to a stimulated series satisfying a diffusion equation allowing us to estimate nonparametrically the drift and volatility functions. The second application involves high frequency data on stock returns.
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Suggested Citation

  • Serge Darolles & Jean-Pierre Florens & Christian Gourieroux, 1998. "Kernel Based Nonlinear Canonical Analysis," Working Papers 98-55, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:98-55
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    1. repec:adr:anecst:y:2007:i:85:p:05 is not listed on IDEAS

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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