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Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models

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Author Info

  • Pradeep Chintagunta

    ()
    (Graduate School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, Illinois 60637)

  • Jean-Pierre Dubé

    ()
    (Graduate School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, Illinois 60637)

  • Khim Yong Goh

    ()
    (Graduate School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, Illinois 60637)

Abstract

We investigate the role of potential weekly brand-specific characteristics that influence consumer choices, but are unobserved or unmeasurable by the researcher. We use an empirical approach, based on the estimation methods used for standard random coefficients logit models, to account for the presence of such unobserved attributes. Using household scanner panel data, we find evidence that ignoring such time-varying latent (to the researcher) characteristics can lead to two types of problems. First, consistent with previous literature, we find that these unobserved characteristics may lead to biased estimates of the mean price response parameters. This argument is based on a form of price endogeneity. If marketing managers set prices based on consumer willingness to pay, then the observed prices will likely be correlated with the latent (to the researcher) brand characteristics. We resolve this problem by using an instrumental variables procedure. Our findings suggest that simply ignoring these attributes may also lead to larger estimates of the variance in the heterogeneity distribution of preferences and price sensitivities across households. This could overstate the benefits from marketing activities such as household-level targeting. We resolve the problem by using weekly brand intercepts, embedded in a random coefficients brand choice model, to control for weekly brand-specific characteristics, while accounting for household heterogeneity. Overall, our results extend the finding on the endogeneity bias from the mean of the heterogeneity distribution (i.e., the price effect) to include the variance of that distribution.

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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 51 (2005)
Issue (Month): 5 (May)
Pages: 832-849

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Handle: RePEc:inm:ormnsc:v:51:y:2005:i:5:p:832-849

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Related research

Keywords: brand choice; choice models; econometric models; targeting; endogeneity; instrumental variables;

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Citations

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Cited by:
  1. Abe Dunn, 2010. "Drug Innovations and Welfare Measures Computed from Market Demand: The Case of Anti-Cholesterol Drugs," BEA Working Papers 0057, Bureau of Economic Analysis.
  2. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics, Springer, vol. 5(3), pages 239-292, September.
  3. Bradlow, E. & Bronnenberg, B.J.J.A.M. & Russell, G.J. & Arora, N. & Bell, D. & Deepak, S.D. & Hofstede, F. ter & Sismeiro, C. & Thomadsen, R. & Yang, S., 2005. "Spatial models in marketing," Open Access publications from Tilburg University urn:nbn:nl:ui:12-332173, Tilburg University.
  4. Christopher T. Conlon & Julie Holland Mortimer, 2010. "Demand Estimation Under Incomplete Product Availability," Boston College Working Papers in Economics 799, Boston College Department of Economics, revised 07 Aug 2012.
  5. Shinya Sugawara, 2013. "Firm-driven Management for Longevity Risk: Analysis of Lump-sum Forward Payments in Japanese Nursing Homes," CIRJE F-Series CIRJE-F-908, CIRJE, Faculty of Economics, University of Tokyo.
  6. Sugawara, Shinya, 2012. "A nonparametric Bayesian approach for counterfactual prediction with an application to the Japanese private nursing home market," MPRA Paper 42154, University Library of Munich, Germany.
  7. Kyoo il Kim & Amil Petrin, 2013. "Tests for Price Endogeneity in Differentiated Product Models," NBER Working Papers 19011, National Bureau of Economic Research, Inc.
  8. Jean-Pierre Dubé & Günter Hitsch & Puneet Manchanda, 2005. "An Empirical Model of Advertising Dynamics," Quantitative Marketing and Economics, Springer, vol. 3(2), pages 107-144, June.
  9. Sergio Meza & K. Sudhir, 2006. "Pass-through timing," Quantitative Marketing and Economics, Springer, vol. 4(4), pages 351-382, December.
  10. Eric Bradlow & Bart Bronnenberg & Gary Russell & Neeraj Arora & David Bell & Sri Duvvuri & Frankel Hofstede & Catarina Sismeiro & Raphael Thomadsen & Sha Yang, 2005. "Spatial Models in Marketing," Marketing Letters, Springer, vol. 16(3), pages 267-278, December.
  11. Sungho Park & Sachin Gupta, 2012. "Comparison of SML and GMM estimators for the random coefficient logit model using aggregate data," Empirical Economics, Springer, vol. 43(3), pages 1353-1372, December.

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