More efficient local polynomial estimation in nonparametric regression with autocorrelated errors
We propose a modification of local polynomial time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that must be estimated from the data. We establish the asymptotic...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
Dec 2003
|
| In: |
Journal of the American Statistical Association
Year: 2003, Volume: 98, Issue: 464, Pages: 980-992 |
| ISSN: | 1537-274X |
| Online Access: | Verlag, Volltext: http://www.jstor.org/stable/30045344 Verlag, Volltext: http://www.jstor.org/stable/pdf/30045344.pdf?refreqid=excelsior:79050497e35fe71e5daeb922943c729f 
|
| Author Notes: | Zhijie Xiao, Oliver B. Linton, Raymond J. Carroll, Enno Mammen |
| Summary: | We propose a modification of local polynomial time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that must be estimated from the data. We establish the asymptotic distribution of our estimator under weak dependence conditions. We show that the proposed estimation procedure is more efficient than the conventional local polynomial method. We also provide simulation evidence to suggest that gains can be achieved in moderate-sized samples. |
|---|---|
| Item Description: | Gesehen am 31.01.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1537-274X |