Robust Time Series Estimation via Weighted Likelihood

C. Agostinelli ()
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C. Agostinelli: University of Venezia, Department of Statistics

A chapter in Developments in Robust Statistics, 2003, pp 1-16 from Springer

Abstract: Summary In this paper we introduce a method for efficient and robust estimation of the unknown parameters of an autoregressive-moving average model based on weighted likelihood. Two types of outliers, i.e. additive and innovation, are taken into account without knowing their number, position or intensity. A new procedure is used to classify the outliers and to bound the impact of additive outliers in order to improve the breakdown point of the method. Two examples and a Monte Carlo simulation are presented.

Keywords: Additive outliers; Autoregressive-moving average model; Innovation outliers; Robust estimation; Weighted likelihood (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_1

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DOI: 10.1007/978-3-642-57338-5_1

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