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.
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Acknowledgements
This research was supported by the grant C26A1145RM of the Universitá di Roma La Sapienza, and the national research PRIN2011 “Forecasting economic and financial time series: understanding the complexity and modelling structural change”, funded by Ministero dell’Istruzione dell’Universitá e della Ricerca.
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Baragona, R., Battaglia, F., Cucina, D. (2014). Maximum Empirical Likelihood Inference for Outliers in Autoregressive Time Series. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-05014-0_4
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DOI: https://doi.org/10.1007/978-3-319-05014-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05013-3
Online ISBN: 978-3-319-05014-0
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