Robustness Analysis in Forecasting of Time Series

Y. Kharin ()
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Y. Kharin: Belarussian State University

A chapter in Developments in Robust Statistics, 2003, pp 180-193 from Springer

Abstract: Summary The problems of statistical forecasting of time series under distortions of traditional hypothetical models are considered. The following distorted models of time series are used: trend models under “outliers” and functional distortions, regression models under “outliers” and “errors-in-regressors”, autoregressive time series with parameter specification errors and non-homogeneous innovations. Robustness characteristics based on the mean square risk of forecasting are introduced and evaluated for these cases. In addition, new robust forecasting procedures are presented.

Keywords: Robustness; Forecasting; Time series; Distortions; Risk (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-57338-5_15

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DOI: 10.1007/978-3-642-57338-5_15

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