 Analyzing the effects of level shifts and temporary changes on the identification of ARIMA modelsF. Javier Trivez and Javier Nievas Journal of Applied Statistics, 1998, vol. 25, issue 3, 409-424 Abstract: The presence of outliers in time series gives rise to important effects on the sample autocorrelation coefficients. In the case where these outliers are not adequately treated, their presence causes errors in the identification of the stochastic process generator of the time series under study. In this respect, Chan has demonstrated that, independent of the underlying process of the outlier-free series, a level shift (LS) at the limit (i.e. asymptotically and considering an LS of a sufficiently large size) will lead to the identification of non-stationary processes; with respect to a temporary change (TC), this will lead, again at the limit, to the identification of an AR(1) autoregressive process with a coefficient equal to the dampening factor that defines this TC. The objective of this paper is to analyze, by way of a simulation exercise, how large the LS and TC present in the time series must be for the limiting result to be relevant, in the sense of seriously affecting the instruments used at the identification stage of the ARIMA models, i.e. the sample autocorrelation function and the sample partial autocorrelation function. Date: 1998 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:25:y:1998:i:3:p:409-424 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
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