Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series |
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Authors: | Roberto Baragona Francesco Battaglia Domenico Cucina |
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Affiliation: | Department of Statistical Sciences, Sapienza University of Rome, Italy |
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Abstract: | Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood‐based method in finite samples and indicates that the proposed methods are preferable when dealing with the non‐Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood. |
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Keywords: | Confidence regions hypothesis testing additive outlier innovation outlier general estimating equations |
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