On robust local polynomial estimation with long-memory errors
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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- 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.
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"Nonparametric M-Estimation with Long-Memory Errors,"
CoFE Discussion Paper
00-19, Center of Finance and Econometrics, University of Konstanz.
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- Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
- 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.
- 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.
- 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.
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