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On subset selection in non-parametric stochastic regression

Author

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  • Yao, Qiwei
  • Tong, Howell

Abstract

This paper is concerned with the use of a cross-validation method based on the kernel estimate of the conditional mean for the subset selection of stochastic regressors within the framework of non-linear stochastic regression. Under the assumption that the observations are strictly stationary and absolutely regular, we show that the cross-validatory selection is consistent. Furthermore, two kinds of asymptotic efficiency of the selected model are proved. Both simulated and real data are used as illustrations.

Suggested Citation

  • Yao, Qiwei & Tong, Howell, 1994. "On subset selection in non-parametric stochastic regression," LSE Research Online Documents on Economics 6409, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:6409
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    File URL: http://eprints.lse.ac.uk/6409/
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    Citations

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    Cited by:

    1. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    2. Tschernig, Rolf & Yang, Lijian, 2000. "Nonparametric estimation of generalized impulse response function," SFB 373 Discussion Papers 2000,89, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Rolf Tschernig & Lijian Yang, 2000. "Nonparametric Lag Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(4), pages 457-487, July.
    4. Gao, Jiti & Tong, Howell, 2002. "Nonparametric and semiparametric regression model selection," MPRA Paper 11987, University Library of Munich, Germany, revised Feb 2004.
    5. Cizek, P. & Hardle, W., 2006. "Robust estimation of dimension reduction space," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 545-555, November.
    6. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    7. Dong, Chaohua & Gao, Jiti & Tong, Howell, 2006. "Semiparametric penalty function method in partially linear model selection," MPRA Paper 11975, University Library of Munich, Germany, revised Aug 2006.
    8. Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
    9. Mayte Suarez -Farinas & Carlos E. Pedreira & Marcelo C. Medeiros, 2004. "Local Global Neural Networks: A New Approach for Nonlinear Time Series Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1092-1107, December.
    10. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," SSE/EFI Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 29 Apr 2004.
    11. Vilar, J.A. & Alonso, A.M. & Vilar, J.M., 2010. "Non-linear time series clustering based on non-parametric forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2850-2865, November.
    12. Rech, Gianluigi & Teräsvirta, Timo & Tschernig, Rolf, 1999. "A simple variable selection technique for nonlinear models," SFB 373 Discussion Papers 1999,26, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    13. Zhou, Yunzhe & Shi, Chengchun & Li, Lexin & Yao, Qiwei, 2023. "Testing for the Markov property in time series via deep conditional generative learning," LSE Research Online Documents on Economics 119352, London School of Economics and Political Science, LSE Library.
    14. Qiang Xia & Kejun He & Cuizhen Niu, 2017. "A Model-Adaptive Test for Parametric Single-Index Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 981-999, November.

    More about this item

    Keywords

    Absolutely regular; cross-validation; efficiency; kernel estimation; heteroscedasticity; non-linear stochastic regression; subset selection;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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