EconPapers    
Economics at your fingertips  
 

On robust local polynominal estimation with long-memory errors

Jan Beran, Yuanhua Feng (), Sucharita Ghosh and Philipp Sibbertsen ()

No 2000,35, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: Prediction 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.

Date: 2000
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/77273/2/2000-35.pdf (application/pdf)

Related works:
Journal Article: On robust local polynomial estimation with long-memory errors (2002) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:200035

Access Statistics for this paper

More papers in Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().

 
Page updated 2019-08-10
Handle: RePEc:zbw:sfb475:200035