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Econometric Models for Forecasting Market Share

  • Chapter
Principles of Forecasting

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 30))

Abstract

By reviewing the literature we developed principles to guide market analysts in their use of econometric models to forecast market share. We rely on the general principles for econometric forecasting developed by Allen and Fildes (2001) to arrive at specific principles. The theoretical and empirical evidence indicates that they should use econometric market share models when

  1. 1.

    effects of current marketing activity are strong relative to the carryover effects of past marketing activity,

  2. 2.

    there are enough observations,

  3. 3.

    the models allow for variation in response for individual brands,

  4. 4.

    the models are estimated using disaggregate (store-level) data rather than aggregate data,

  5. 5.

    the data exhibit enough variability, and

  6. 6.

    competitors’ actions can be forecast with reasonable accuracy.

In most situations the first five conditions can be satisfied. Condition 6 is more difficult to satisfy and is a priority area for further research. If one or more of the conditions are not satisfied then an extrapolation or judgment forecasting method may be more appropriate.

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Author information

Authors and Affiliations

  1. University of Auckland, New Zealand

    Roderick J. Brodie & Peter J. Danaher

  2. University of Houston, USA

    V. Kumar

  3. University of Groningen, The Netherlands

    Peter S. H. Leeflang

Authors
  1. Roderick J. Brodie
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  2. Peter J. Danaher
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  3. V. Kumar
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  4. Peter S. H. Leeflang
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Editor information

Editors and Affiliations

  1. The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    J. Scott Armstrong

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© 2001 Springer Science+Business Media New York

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Brodie, R.J., Danaher, P.J., Kumar, V., Leeflang, P.S.H. (2001). Econometric Models for Forecasting Market Share. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-306-47630-3_27

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  • DOI: https://doi.org/10.1007/978-0-306-47630-3_27

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-7401-5

  • Online ISBN: 978-0-306-47630-3

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