On robust local polynomial estimation with long-memory errors
AbstractPrediction in time series models with a trend requires reliable estimation 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-estimation is considered for the case where the error process exhibits long-range dependence. In constrast to the iid case, all 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 bandwidth selection, in particular due to the change of the dependence structure. --
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 18 (2002)
Issue (Month): 2 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Other versions of this item:
- Jan Beran & Yuanhua Feng & Sucharita Gosh & Philipp Sibbertsen, 2000. "On robust local polynomial estimation with long-memory errors," CoFE Discussion Paper 00-18, Center of Finance and Econometrics, University of Konstanz.
- Beran, Jan & Feng, Yuanhua & Ghosh, Sucharita & Sibbertsen, Philipp, 2000. "On robust local polynominal estimation with long-memory errors," Technical Reports 2000,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
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- 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.
- Beran, Jan & Ghosh, Sucharita & Sibbertsen, Philipp, 2000.
"Nonparametric M-estimation with long-memory errors,"
2000,36, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Jan Beran & Sucharita Gosh & Philipp Sibbertsen, 2000. "Nonparametric M-Estimation with Long-Memory Errors," CoFE Discussion Paper 00-19, Center of Finance and Econometrics, University of Konstanz.
- Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer, vol. 54(2), pages 291-311, June.
- Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
- Heni Boubaker & Nadia Sghaier, 2014. "Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach," Working Papers 2014-066, Department of Research, Ipag Business School.
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- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
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