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Identification non paramétrique d’un processus non linéaire hétéroscédastique
[Nonparametric identification of heteroscedastic nonlinear process]

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  • CHIKHI, Mohamed

Abstract

Cet article vise à identifier un processus non linéaire par la méthode du noyau. Cette identification nécessite une sélection rigoureuse des coefficients de Markov et le choix de la fenêtre qui détermine le degré de lissage de l’estimateur. This paper aims to identify a nonlinear process by the kernel methodology. This identification requires the selection of the Markov coefficients and the choice of bandwidth, which determines the degree of estimator’s smoothing.

Suggested Citation

  • CHIKHI, Mohamed, 2009. "Identification non paramétrique d’un processus non linéaire hétéroscédastique [Nonparametric identification of heteroscedastic nonlinear process]," MPRA Paper 82108, University Library of Munich, Germany, revised 2009.
    • Handle: RePEc:pra:mprapa:82108
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    File URL: https://mpra.ub.uni-muenchen.de/82108/1/1104.pdf
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    References listed on IDEAS

    as
    1. Ullah, Aman, 1988. "Nonparametric Estimation and Hypothesis Testing in Econometric Models," Empirical Economics, Springer, vol. 13(3/4), pages 223-249.
    2. Lijian Yang & Wolfgang Hardle & Jens Nielsen, 1999. "Nonparametric Autoregression with Multiplicative Volatility and Additive mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(5), pages 579-604, September.
    3. L. Yang & R. Tschernig, 1999. "Multivariate bandwidth selection for local linear regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 793-815.
    4. Chiu, Shean-Tsong, 1989. "Bandwidth selection for kernel estimate with correlated noise," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 347-354, September.
    5. Bruce Mizrach, 1995. "A Simple Nonparametric Test for Independence," Departmental Working Papers 199523, Rutgers University, Department of Economics.
    6. Härdle, Wolfgang & Chen, R., 1995. "Nonparametric Time Series Analysis, a selectiv review with examples," SFB 373 Discussion Papers 1995,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    8. Yang, Lijian & Tschernig, Rolf, 2002. "Non- And Semiparametric Identification Of Seasonal Nonlinear Autoregression Models," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1408-1448, December.
    9. Wolfgang Härdle & Helmut Lütkepohl & Rong Chen, 1997. "A Review of Nonparametric Time Series Analysis," International Statistical Review, International Statistical Institute, vol. 65(1), pages 49-72, April.
    10. Tschernig, R., 1996. "Nonlinearities in German Unemployment Rates: A Nonparametric Analysis," SFB 373 Discussion Papers 1996,45, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    11. P. M. Robinson, 1983. "Nonparametric Estimators For Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(3), pages 185-207, May.
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    More about this item

    Keywords

    Final Prediction Error; kernel; bandwidth; functional autoregressive process.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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