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On robust local polynomial estimation with long-memory errors

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Author Info
Jan Beran () (Center of Finance and Econometrics)
Yuanhua Feng () (Center of Finance and Econometrics)
Sucharita Gosh (Landscape Department, Swiss Federal Research Institute WSL)
Philipp Sibbertsen (Fachbereich Statistik, Universität Dortmund)

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Abstract

Prediction in time series models with a trend requires reliable estima- tion of the trend function at the right end of the observed series. Local polynomial smoothing is a suitable tool because boundary corrections are included implicitly. However, outliers may lead to unreliable estimates, if least squares regression is used. In this paper, local polynomial smoothing based on M-estimators are asymptotically equivalent to the least square solution, under the (ideal) Gaussian model. Outliers turn out to have a major effect on nonrobust bandwidht selection, in particular due to the change of the dependence structure.

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Publisher Info
Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 00-18.

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Length: 17 Pages
Date of creation: Jun 2000
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Handle: RePEc:knz:cofedp:0018

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  1. Jan Beran, 1999. "SEMIFAR Models - A Semiparametric Framework for Modelling Trends, Long Range Dependence and Nonstationarity," CoFE Discussion Paper 99-16, Center of Finance and Econometrics, University of Konstanz. [Downloadable!]
  2. Jan Beran & Dirk Ocker, 1999. "SEMIFAR Forecasts, with Applications to Foreign Exchange Rates," CoFE Discussion Paper 99-13, Center of Finance and Econometrics, University of Konstanz. [Downloadable!]
  3. Giraitis, Liudas & Koul, Hira L. & Surgailis, Donatas, 1996. "Asymptotic normality of regression estimators with long memory errors," Statistics & Probability Letters, Elsevier, vol. 29(4), pages 317-335, September. [Downloadable!] (restricted)
  4. Jan Beran & Yuanhua Feng, 2000. "Data-driven estimation of semiparametric fractional autoregressive models," CoFE Discussion Paper 00-16, Center of Finance and Econometrics, University of Konstanz. [Downloadable!]
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