Bayesian Semiparametric Regression: An Exposition and Application to Print Advertising Data

A new regression based approach is proposed for modeling marketing databases. The approach is Bayesian and provides a number of significant improvements over current methods. Independent variables can enter into the model in either a parametric or nonparametric manner, significant variables can be identified from a large number of potential regressors and an appropriate transformation of the dependent variable can be automatically selected from a discrete set of pre-specified candidate transformations. All these features are estimated simultaneously and automatically using a Bayesian hierarchical model coupled with a Gibbs sampling scheme. Being Bayesian, it is straightforward to introduce subjective information about the relative importance of each variable, or with regard to a suitable data transformation. The methodology is applied to print advertising Starch data collected from thirteen issues of an Australian women's monthly magazine. The empirical results highlight the complex and detailed relationships that can be uncovered using the methodology.


Issue Date:
Nov 01 1997
Publication Type:
Working or Discussion Paper
Record Identifier:
http://ageconsearch.umn.edu/record/267935
Language:
English
Total Pages:
44
Series Statement:
Working Paper 13/97




 Record created 2018-02-06, last modified 2018-02-07

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