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Beyond mean estimates of price and promotional effects in scanner-panel sales–response regression

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  • Haupt, Harry
  • Kagerer, Kathrin

Abstract

Traditional mean estimates of conditional sales given price and promotion variables may provide misleading guidance about the underlying market mechanisms, since high, low, and medium sales, respectively, may be generated by quite different price and promotion strategies. Empirical evidence for consumer good scanner data reveals nonlinearities and heteroskedasticity in the sales–response relationship—mean effects typically average and hence may obscure a potentially rich nature of observational data. Besides addressing the heterogeneity of price and promotional effects, the proposed quantile regression framework allows direct estimation of monotonicity restricted nonlinear pricing effects for quantiles of the sales distribution.

Suggested Citation

  • Haupt, Harry & Kagerer, Kathrin, 2012. "Beyond mean estimates of price and promotional effects in scanner-panel sales–response regression," Journal of Retailing and Consumer Services, Elsevier, vol. 19(5), pages 470-483.
    • Handle: RePEc:eee:joreco:v:19:y:2012:i:5:p:470-483
    DOI: 10.1016/j.jretconser.2012.06.002
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    References listed on IDEAS

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    1. Fok, Dennis & Hans Franses, Philip & Paap, Richard, 2007. "Seasonality and non-linear price effects in scanner-data-based market-response models," Journal of Econometrics, Elsevier, vol. 138(1), pages 231-251, May.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
    3. Brezger, Andreas & Steiner, Winfried J., 2008. "Monotonic Regression Based on Bayesian PSplines: An Application to Estimating Price Response Functions From Store-Level Scanner Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 90-104, January.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
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    Cited by:

    1. Ronald B. Larson, 2019. "Promoting demand-based pricing," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 42-51, February.
    2. Marcial López-Pastor & Jesús García-Madariaga & Joaquín Sánchez & Jose Figueiredo, 2020. "Demand Impact for Prices Ending with “9” and “0” in Online and Offline Consumer Goods Retail Trade Channels," International Review of Management and Marketing, Econjournals, vol. 10(6), pages 58-78.
    3. Philipp Aschersleben & Winfried J. Steiner, 2022. "A semiparametric approach to estimating reference price effects in sales response models," Journal of Business Economics, Springer, vol. 92(4), pages 591-643, May.

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