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Outliers in Time Series: An Empirical Likelihood Approach

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Topics on Methodological and Applied Statistical Inference

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

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Abstract

The empirical likelihood method is known to be a flexible and effective approach for testing hypotheses and building confidence regions in a nonparametric setting. This framework is adopted here for dealing with the outlier problem in time series where conventional distributional assumptions may be inappropriate in most cases. The procedure is illustrated by a simulation experiment. The results are also supported by the study of two well-known real-time series data: the fossil marine families extinction rates and the Nile river volume at Aswan 1871–1970.

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Acknowledgments

This research was supported by the grant C26A1145RM of the Università di Roma La Sapienza, and the national research PRIN2011 “Forecasting economic and financial time series: understanding the complexity and modeling structural change”, funded by Ministero dell’Istruzione dell’Università e della Ricerca.

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Authors and Affiliations

  1. Department of Economic and Political Sciences and Modern Languages, Lumsa University of Rome, Rome, Italy

    Roberto Baragona

  2. Department of Statistical Sciences, La Sapienza, University of Rome, Rome, Italy

    Domenico Cucina

Authors
  1. Roberto Baragona
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  2. Domenico Cucina
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Corresponding author

Correspondence to Roberto Baragona .

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Editors and Affiliations

  1. Universita delgi Studi G. "d'Annunzio" chieti pescara, Pescara, Italy

    Tonio Di Battista

  2. Statistics & Operations Research, University of Granada, Granada, Spain

    Elías Moreno

  3. Department of Mathematics, University of Cagliari Department of Mathematics, Cagliari, Italy

    Walter Racugno

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Baragona, R., Cucina, D. (2016). Outliers in Time Series: An Empirical Likelihood Approach. In: Di Battista, T., Moreno, E., Racugno, W. (eds) Topics on Methodological and Applied Statistical Inference. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-44093-4_3

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