The Identification of Multiple Outliers in ARIMA Models
By María Jesús Sánchez, D. Pena. 2003
53 citations recorded by the source corpus.
Abstract There are three main problems in the existing procedures for detecting outliers in ARIMA models. The first one is the biased estimation of the initial parameter values that may strongly affect the power to detect outliers. The second problem is the confusion between level shifts and innovative outliers when the series has a level shift. The third problem is masking. We propose a procedure that keeps the powerful features of previous methods but improves the initial parameter estimate, avoids the confusion between innovative outliers and level shifts and includes joint tests for sequences of additive outliers in order to solve the masking problem. A Monte Carlo study and one example of the performance of the proposed procedure are presented.
Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/3db86b1b5dd377699fbb5ca17a697f3dfa2d157b
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In Electrical and Electronic Engineering
In Finance
In Management Science and Operations Research
Citations in the corpus
- An Automatic Test of Super Exogeneity (2010, background): “Panel b shows that no outliers, as defined by|ûi,t| > 2σii (say), are detected (for an alternative approach, see Sánchez and Pe˜na, 2003).”