Maximum Empirical Likelihood Inference for Outliers in Autoregressive Time Series

Roberto Baragona (), Francesco Battaglia () and Domenico Cucina ()
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Roberto Baragona: Lumsa University of Rome
Francesco Battaglia: Sapienza University of Rome
Domenico Cucina: Sapienza University of Rome

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2014, pp 17-20 from Springer

Abstract: Abstract Outliers in time series are usually distinguished in additive, innovation, and transient and permanent change. An approach based on empirical likelihood is presented for estimating outliers of the four types in a linear autoregressive time series. Theoretical results are illustrated along with hints for future research.

Keywords: Additive and innovation outlier; Level change; Confidence intervals (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-05014-0_4

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DOI: 10.1007/978-3-319-05014-0_4

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