Marketing Letters

, Volume 4, Issue 3, pp 227–239 | Cite as

A marginal-predictive approach to identifying household parameters

  • Greg M. Allenby
  • Peter E. Rossi


Estimation of household parameters in scanner panel data requires the introduction of prior information. Traditionally, prior information is incorporated by restricting parameters to be constant across households or by specifying a random coefficient distribution. An alternative solution is to incorporate stochastic prior information in a formal Bayesian approach. In standard Bayesian analysis, a prior distribution over the model parameters is specified and combined with the household likelihood to obtain the Bayes estimates. The construction of the prior distribution over model parameters may be difficult, especially when working with new models whose parameters are difficult to interpret. In this paper, we propose a solution which specifies prior information through the marginal distribution of the data, i.e., the outcomes. We evaluate this marginal-predictive approach, using both actual and simulated panel data, and show it to be highly accurate relative to other available alternatives.


Panel Data Prior Distribution Coefficient Distribution Bayesian Approach Bayesian Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Fabey, Alison and Bradley Johnson. (1990). “Frequent Shopper Programs Ripen,”Advertising Age 61 (August 6), p. 21.Google Scholar
  2. Akaike, Hirotugu. (1974). “A New Look at the Statistical Model Identification,”IEEE Transactions on Automatic Control AC-19, 6, 716–23.Google Scholar
  3. Allenby, Greg M. and James L. Ginter. (1992). “Modeling Competitive Subsets and Product Differentiation.” Working Paper, College of Business, The Ohio State University.Google Scholar
  4. Berger, James O. (1985).Statistical Decision Theory and Bayesian Analysis. 2nd ed. New York: Springer-Verlag.Google Scholar
  5. Clarkson, D. B. and R. I. Jennrich (1991). “Computing Extended Maximum Likelihood Estimates for Linear Parameter Models,”Journal of the Royal Statistical Society B, 53, 417–426.Google Scholar
  6. Guadagni, Peter M. and John D. C. Little. (1983). “A Logit Model of Brand Choice Calibrated on Scanner Data,”Marketing Science 2 (3), 203–38.Google Scholar
  7. Kamakura, Wagner A. and Gary J. Russell. (1989). “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure,”Journal of Marketing Research 26 (November), 379–90.Google Scholar
  8. Krishnamurthi, Lakshman and S. P. Raj. (1988). “A Model of Brand Choice and Purchase Quantity Price Sensitivities,”Marketing Science 7, 1, 1–20.Google Scholar
  9. Kullback, Solomon. (1959).Information Theory and Statistics. Gloucester, Mass.: Peter Smith.Google Scholar
  10. McFadden, Daniel. (1973). “Conditional Logit Analysis of Qualitative Choice Behavior.” In P. Zarembka (ed.),Frontiers of Econometrics. New York: Academic Press.Google Scholar
  11. McFadden, Daniel. (1984). “Econometric Analysis of Qualitative Response Models.” In Z. Griliches and M. Intriligator (eds.),Handbook of Econometrics, Vol. II. Amesterdam: North-Holland, 1395–1457.Google Scholar
  12. Rossi, Peter E. and Greg M. Allenby. (1993). “A Bayesian Approach to Estimating Household Parameters,”Journal of Marketing Research xxx, 171–182.Google Scholar
  13. Schwarz, Gideon. (1978). “Estimating the Dimension of a Model,”The Annals of Statistics 6, 461–464.Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Greg M. Allenby
    • 1
  • Peter E. Rossi
    • 2
  1. 1.College of BusinessOhio State UniversityColumbus
  2. 2.Graduate School of BusinessUniversity of ChicagoChicago

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