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The Reliability and Accuracy of Time Series Model Identification

Author

Listed:
  • Wayne F. Velicer

    (University of Rhode Island)

  • John Harrop

    (University of Rhode Island)

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 .

Suggested Citation

  • Wayne F. Velicer & John Harrop, 1983. "The Reliability and Accuracy of Time Series Model Identification," Evaluation Review, , vol. 7(4), pages 551-560, August.
    • Handle: RePEc:sae:evarev:v:7:y:1983:i:4:p:551-560
    DOI: 10.1177/0193841X8300700408
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    References listed on IDEAS

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    1. Nerlove, Marc & Grether, David M. & Carvalho, José L., 1979. "Analysis of Economic Time Series," Elsevier Monographs, Elsevier, edition 1, number 9780125157506 edited by Shell, Karl.
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    2. Ariel Linden, 2017. "A comprehensive set of postestimation measures to enrich interrupted time-series analysis," Stata Journal, StataCorp LP, vol. 17(1), pages 73-88, March.

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