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Predictability and Prediction

Author

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  • A. S. C. Ehrenberg
  • J. A. Bound

Abstract

A result can be regarded as routinely predictable when it has recurred consistently under a known range of different conditions. This depends on the previous analysis of many sets of data, drawn from different populations. There is no such basis of extensive experience when a prediction is derived from the analysis of only a single set of data. Yet that is what is mainly discussed in our statistical texts. The paper discusses the design and analysis of studies aimed at achieving routinely predictable results. It uses two running case history examples.

Suggested Citation

  • A. S. C. Ehrenberg & J. A. Bound, 1993. "Predictability and Prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(2), pages 167-194, March.
    • Handle: RePEc:bla:jorssa:v:156:y:1993:i:2:p:167-194
    DOI: 10.2307/2982727
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    Cited by:

    1. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    2. Gavin Lees & Maxwell Winchester & Sidath Silva, 2016. "Demographic product segmentation in financial services products in Australia and New Zealand," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(3), pages 240-250, September.
    3. Paul B Conn & Devin S Johnson & Peter L Boveng, 2015. "On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.
    4. Zachary Anesbury & Maxwell Winchester & Rachel Kennedy, 2017. "Brand user profiles seldom change and seldom differ," Marketing Letters, Springer, vol. 28(4), pages 523-535, December.
    5. Lindsay, R. Murray, 1995. "Reconsidering the status of tests of significance: An alternative criterion of adequacy," Accounting, Organizations and Society, Elsevier, vol. 20(1), pages 35-53, January.
    6. Hubbard, Raymond & Lindsay, R. Murray, 2013. "The significant difference paradigm promotes bad science," Journal of Business Research, Elsevier, vol. 66(9), pages 1393-1397.
    7. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).
    8. Hubbard, Raymond & Vetter, Daniel E., 1996. "An empirical comparison of published replication research in accounting, economics, finance, management, and marketing," Journal of Business Research, Elsevier, vol. 35(2), pages 153-164, February.
    9. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.
    10. Jella Pfeiffer & Thies Pfeiffer & Martin Meißner & Elisa Weiß, 2020. "Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments," Information Systems Research, INFORMS, vol. 31(3), pages 675-691, September.
    11. Uncles, Mark D. & Kwok, Simon, 2013. "Designing research with in-built differentiated replication," Journal of Business Research, Elsevier, vol. 66(9), pages 1398-1405.
    12. R. Murray Lindsay, 1994. "Publication System Biases Associated with the Statistical Testing Paradigm," Contemporary Accounting Research, John Wiley & Sons, vol. 11(1), pages 33-57, June.
    13. Phua, Peilin & Kennedy, Rachel & Trinh, Giang & Page, Bill & Hartnett, Nicole, 2020. "Examining older consumers’ loyalty towards older brands in grocery retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    14. Page, Bill & Sharp, Anne & Lockshin, Larry & Sorensen, Herb, 2018. "Parents and children in supermarkets: Incidence and influence," Journal of Retailing and Consumer Services, Elsevier, vol. 40(C), pages 31-39.
    15. Hubbard, Raymond & Lindsay, R. Murray, 2013. "From significant difference to significant sameness: Proposing a paradigm shift in business research," Journal of Business Research, Elsevier, vol. 66(9), pages 1377-1388.
    16. Gaunt, J. L. & Riley, Janet & Stein, A. & Penning de Vries, F. W. T., 1997. "Requirements for effective modelling strategies," Agricultural Systems, Elsevier, vol. 54(2), pages 153-168, June.

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