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A comparative study of market share models using disaggregate data

Author

Listed:
  • V. Kumar

    (College of Business Administration - University of Houston)

  • Timothy B. Heath

    (Graduate School of Business - PITT - University of Pittsburgh - Pennsylvania Commonwealth System of Higher Education (PCSHE))

Abstract

Prior research assessing the predictive validity of alternate market share models produced conflicting results and often found that econometric models performed worse than naive extrapolations. However, contributors to IJF's recent issue on market share models suggested that such models are often misspecified, in part because they exclude promotional variables and are estimated on aggregate data. Thus, we used weekly scanner data to assess full, reduced, and naive forms of linear, multiplicative, and attraction specifications across different levels of parameterization. Consistent with specification-based arguments, (1) econometric models were superior to naive models, (2) GLS estimates of attraction models were superior when models were fully specified, (3) OLS estimates of linear models were superior when models omitted important variables, and (4) attraction models predicted best overall. Moreover, in general, unconstrained models yielded superior forecasts relative to constrained models because brand-specific parameters were heterogeneous for the product category tested.

Suggested Citation

  • V. Kumar & Timothy B. Heath, 1990. "A comparative study of market share models using disaggregate data," Post-Print hal-00670544, HAL.
    • Handle: RePEc:hal:journl:hal-00670544
    DOI: 10.1016/0169-2070(90)90002-S
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    Citations

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    Cited by:

    1. Klapper, Daniel & Herwartz, Helmut, 1998. "Forecasting performance of market share attraction models: A comparison of different models assuming that competitors' actions are forecasts," SFB 373 Discussion Papers 1998,103, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Klapper, Daniel & Herwartz, Helmut, 2000. "Forecasting market share using predicted values of competitive behavior: further empirical results," International Journal of Forecasting, Elsevier, vol. 16(3), pages 399-421.
    3. Rutger Oest, 2005. "Which Brands Gain Share from Which Brands? Inference from Store-Level Scanner Data," Quantitative Marketing and Economics (QME), Springer, vol. 3(3), pages 281-304, September.
    4. Kumar, V. & Leone, Robert P. & Gaskins, John N., 1995. "Aggregate and disaggregate sector forecasting using consumer confidence measures," International Journal of Forecasting, Elsevier, vol. 11(3), pages 361-377, September.
    5. Kattuman, P. & Roberts, B.M., 2000. "Strategy Choices of Firms and Market Concentration'," Cambridge Working Papers in Economics 0018, Faculty of Economics, University of Cambridge.
    6. Carl Rudolf Blankart & Tom Stargardt, 2017. "Preferred supplier contracts in post-patent prescription drug markets," Health Care Management Science, Springer, vol. 20(3), pages 419-432, September.
    7. van Oest, R.D. & Franses, Ph.H.B.F., 2003. "Which brands gain share from which brands? Inference from store-level scanner data," ERIM Report Series Research in Management ERS-2003-076-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Siotis, Georges & Martinez Granado, Maite, 2006. "Computing Abuse Related Damages in the Case of New Entry: An Illustration for the Directory Enquiry Services Market," CEPR Discussion Papers 5813, C.E.P.R. Discussion Papers.
    9. Kumar, V. & Nagpal, Anish & Venkatesan, Rajkumar, 2002. "Forecasting category sales and market share for wireless telephone subscribers: a combined approach," International Journal of Forecasting, Elsevier, vol. 18(4), pages 583-603.
    10. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
    11. Derek W. Bunn & Stefania Pantelidaki, 2005. "Development of a multifunctional sales response model with the diagnostic aid of artificial neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 505-521.

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