Robust forecasting of non-stationary time series
AbstractThis paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estimator. An additional advantage of the MM-estimator is that it provides a robust estimate of the local variability of the time series.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Katholieke Universiteit Leuven in its series Open Access publications from Katholieke Universiteit Leuven with number urn:hdl:123456789/277099.
Date of creation: Sep 2010
Date of revision:
Contact details of provider:
Web page: http://www.kuleuven.be
Heteroscedasticity; Non-parametric regression; Prediction; Outliers; Robustness;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-10-16 (All new papers)
- NEP-ECM-2010-10-16 (Econometrics)
- NEP-ETS-2010-10-16 (Econometric Time Series)
- NEP-FOR-2010-10-16 (Forecasting)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Carl Demeyere).
If references are entirely missing, you can add them using this form.