.png){kind=icon}
.png){kind=icon}
Return
On robust local polynomial estimation with long-memory errors
.png){kind=icon}
OA
AI
DOI：10.1016/S0169-2070(01)00155-8.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 contrast to the iid case, all M-estimators are asymptotically equivalent to the least-square solution. under the (ideal) Gaussian model, The potential usefulness of the proposal for forecasting is illustrated by practical and simulated examples. A simulation study shows that outliers have a major effect on nonrobust bandwidth selection, in particular due to the change of the dependence structure, (C) 2002 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.
Keywords:
local polynomial
long memory
M-estimator
SEMIFAR models
forecasting
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
3.1K
Citations:
9.9K
Organization
No organization information available