The Reliability and Accuracy of Time Series Model Identification

Wayne F. Velicer and John Harrop
Additional contact information
Wayne F. Velicer: University of Rhode Island
John Harrop: University of Rhode Island

Evaluation Review, 1983, vol. 7, issue 4, 551-560

Abstract: The most widely employed procedure for interrupted time series analysis consists of a two-step procedure: (1) determining the ARIMA model by examining the pattern of autocorrelations and partial autocorrelations; and (2) employing a general linear model solution after the effect of dependency has been removed. In order to determine the reliability and accuracy of model identification, 12 extensively trained subjects were each asked to identify 32 different computer generated time series. Six commonly occurring models were employed with different levels of dependency (high, medium, or low) and different numbers of data points (N=40 and N=100). The overall accuracy, 28%, was affected by the number of data points, the type of model, and the degree of dependency .

Date: 1983
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0193841X8300700408 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:7:y:1983:i:4:p:551-560

DOI: 10.1177/0193841X8300700408

Access Statistics for this article

More articles in Evaluation Review
Bibliographic data for series maintained by SAGE Publications ().