 A comparison of some of pattern identification methods for order determination of mixed ARMA modelsWai-Sum Chan Statistics & Probability Letters, 1999, vol. 42, issue 1, 69-79 Abstract: Model identification is a crucial step in time-series modelling. The orthodox Box-Jenkins (BJ) identification examines the patterns of the sample autocorrelation function (SACF) and the sample partial autocorrelation function (SPACF). However, for mixed ARMA processes, the SACF and SPACF often exhibit similar behaviour, which makes the identification much more difficult. Recently, identification methods using the patterns of some functions of the autocorrelations have been proposed to supplement the BJ methods. This paper studies some of these proposed procedures. Their performances for order selection of a mixed ARMA process are compared with an expert system in a simulation study. Comments on each individual identification method are also given. Keywords: Corner; method; ESACF; table; Model; identification; Outliers; SCAN; table; Time-series; ARMA; models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:42:y:1999:i:1:p:69-79 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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