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On subset selection in non-parametric stochastic regression

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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.

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File URL: http://eprints.lse.ac.uk/6409/1/On_subset_selection_in_non-parametric_stochastic_regression%28LSERO%29.pdf
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Bibliographic Info

Paper provided by London School of Economics and Political Science in its series Open Access publications from London School of Economics and Political Science with number http://eprints.lse.ac.uk/6409/.

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Date of creation: Jan 1994
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Publication status: Published in Statistica Sinica (1994-01) v.4, p.51-70
Handle: RePEc:ner:lselon:http://eprints.lse.ac.uk/6409/

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Web page: http://www.lse.ac.uk

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Cited by:
  1. Rolf Tschernig & Lijian Yang, 2000. "Nonparametric Estimation of Generalized Impulse Response Functions," Econometric Society World Congress 2000 Contributed Papers 1417, Econometric Society.
  2. 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.
  3. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 10 Feb 2000.
  4. Tschernig, Rolf & Yang, Lijian, 1997. "Nonparametric lag selection for time series," SFB 373 Discussion Papers 1997,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  5. Cai, Zongwu, 2003. "Trending Time-Varying Coefficient Models With Serially Correlated Errors," SFB 373 Discussion Papers 2003,7, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  6. 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.

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