Robust estimators under semi‐parametric partly linear autoregression: Asymptotic behaviour and bandwidth selection

Ana Bianco and Graciela Boente

Journal of Time Series Analysis, 2007, vol. 28, issue 2, 274-306

Abstract: Abstract. 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.

Date: 2007
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Citations: View citations in EconPapers (10)

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https://doi.org/10.1111/j.1467-9892.2006.00511.x

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:28:y:2007:i:2:p:274-306

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