Outliers in functional autoregressive time series

Francesco Battaglia

Statistics & Probability Letters, 2005, vol. 72, issue 4, 323-332

Abstract: A method for identifying and estimating outliers in a time series is proposed, based on fitting functional autoregressive models. Both additive and innovation outliers may be defined. A simulation experiment and the analysis of some real data sets suggest that the proposed method is effective both for series following some nonlinear models, such as self-exciting threshold autoregressive or exponential autoregressive, and for linear series generated by autoregressive moving average processes.

Keywords: Additive; and; innovation; outliers; Local; linear; regression; Nonlinear; models; Varying-coefficient; models (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (1)

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