Outliers & predicting time series: A comparative study

Vlad Ardelean and Thomas Pleier

No 05/2013, FAU Discussion Papers in Economics from Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics

Abstract: Nonparametric prediction of time series is a viable alternative to parametric prediction, since parametric prediction relies on the correct specification of the process, its order and the distribution of the innovations. Often these are not known and have to be estimated from the data. Another source of nuisance can be the occurrence of outliers. By using nonparametric methods we circumvent both problems, the specification of the processes and the occurrence of outliers. In this article we compare the prediction power for parametric prediction, semiparametric prediction and nonparamatric methods such as support vector machines and pattern recognition. To measure the prediction power we use the MSE. Furthermore we test if the increase in prediction power is statistically significant.

Keywords: Parametric prediction; Nonparametric prediction; Support Vector Regression; Outliers (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/76661/1/747831459.pdf (application/pdf)

Related works:
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:iwqwdp:052013

Access Statistics for this paper

More papers in FAU Discussion Papers in Economics from Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().