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Ordinal Time Series Methodology for Industry and Competitive Analysis

Author

Listed:
  • Timothy W. Ruefli

    (IC 2 Institute, The University of Texas, Austin, Texas 78705-3594)

  • Chester L. Wilson

    (IC 2 Institute, The University of Texas, Austin, Texas 78705-3594)

Abstract

Ordinal time series analysis is a new methodology that is especially appropriate for industry and competitive analysis along multiple dimensions of performance over periods of time. The methodology, by using ordinal data, eliminates the requirements encountered in cardinal analysis for further assumptions and calculations concerning model specifications, appropriate discount rates, and re-scaling of data to facilitate presentation. Using longitudinal data, ordinal time series analysis allows the strategy analyst to develop empirical measures of the position, volatility, direction of movement, and relative uncertainty associated with an industry or with groups of firms within that industry. Using these statistical patterns of industry behavior, key strategic dimensions and relationships among and across performance measures, groups of firms, and industries can be identified. The methodology is illustrated by using published data to perform a comparative ordinal time series analysis of the largest firms in the transportation industry over the last quarter century.

Suggested Citation

  • Timothy W. Ruefli & Chester L. Wilson, 1987. "Ordinal Time Series Methodology for Industry and Competitive Analysis," Management Science, INFORMS, vol. 33(5), pages 640-661, May.
    • Handle: RePEc:inm:ormnsc:v:33:y:1987:i:5:p:640-661
    DOI: 10.1287/mnsc.33.5.640
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    Citations

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    Cited by:

    1. Wallace, R. S. Olusegun & Choudhury, Mohammed S. I. & Adhikari, Ajay, 1999. "The Comprehensiveness of Cash Flow Reporting in the United Kingdom: Some Characteristics and Firm-specific Determinants," The International Journal of Accounting, Elsevier, vol. 34(3), pages 311-347, August.
    2. John W. Boudreau, 2004. "50th Anniversary Article: Organizational Behavior, Strategy, Performance, and Design in Management Science," Management Science, INFORMS, vol. 50(11), pages 1463-1476, November.
    3. Jones, Calvin & Jordan, Declan, 2014. "Competitiveness in Soccer Leagues: An ordinal time series approach with evidence from the Premier League 1993 to 2014," MPRA Paper 61193, University Library of Munich, Germany, revised Dec 2014.
    4. Ahmet M. Çilingirtürk & Habip Koçak, 2018. "Human Development Index (HDI) Rank-Order Variability," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(2), pages 481-504, June.
    5. Luís Cabral & Gabriel Natividad, 2016. "Box-Office Demand: The Importance of Being #1," Journal of Industrial Economics, Wiley Blackwell, vol. 64(2), pages 277-294, June.
    6. Gregory D. Kane & Frederick M. Richardson & Nancy L. Meade, 1998. "Rank Transformations and the Prediction of Corporate Failure," Contemporary Accounting Research, John Wiley & Sons, vol. 15(2), pages 145-166, June.

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