Robust estimators under semi-parametric partly linear autoregression: Asymptotic behaviour and bandwidth selection
In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smooth...
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| Autores principales: | , |
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| Formato: | JOUR |
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_01439782_v28_n2_p274_Bianco |
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| Sumario: | In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smoothing techniques are combined. Asymptotic results on the autoregression estimators are derived. Besides combining robust procedures with M-smoothers, predicted values for the series and detection residuals, which allow to detect anomalous data, are introduced. Robust cross-validation methods to select the smoothing parameter are presented as an alternative to the classical ones, which are sensitive to outlying observations. A Monte Carlo study is conducted to compare the performance of the proposed criteria. Finally, the asymptotic distribution of the autoregression parameter estimator is stated uniformly over the smoothing parameter. © 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd. |
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