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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
Article

Abstract

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.

Keywords

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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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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