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Nonparametric analysis of stochastic systems with nonlinear functional heterogeneity


  • Malugin, Vladimir

    () (Belarusian State University)

  • Vasilkov, Mikhail

    () (Belarusian State University)


The problems of the analysis of stochastic systems described by nonlinear statistical models with heterogeneous functional forms are considered in the space of «essential dependent» features. It is supposed that functional heterogeneity is conditioned by the existing of the different classes of system states. The algorithm of classification of the systems states as well as the forecasting algorithm for endogenous variables based on multivariate nonparametric density estimate with adaptive kernel are described and examined by means of statistical modeling experiments

Suggested Citation

  • Malugin, Vladimir & Vasilkov, Mikhail, 2011. "Nonparametric analysis of stochastic systems with nonlinear functional heterogeneity," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 22(2), pages 78-92.
  • Handle: RePEc:ris:apltrx:0075

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    References listed on IDEAS

    1. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    2. Jeffrey Racine, 2008. "Nonparametric econometrics: a primer (in Russian)," Quantile, Quantile, issue 4, pages 7-56, March.
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    More about this item


    multivariate model; essential dependent features; functional heterogeneity; multivariate nonparametric density estimate; adaptive Gaussian kernel; nonparametric classification and forecasting;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis


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