A Heterogeneous Bayesian Regression Model for Cross-sectional Data Involving a Single Observation per Response Unit

D. K. H. Fong, P. EBBES, W. DeSarbo


April 2012, vol. 77, n°2, pp.293-314

Departments: Marketing, GREGHEC (CNRS)

Keywords: Bayesian estimation, Cross-sectional analysis, Heterogeneity, Consumer psychology

pas sous affiliation HECAbstract: Multiple regression is frequently used across the various social sciences to analyze cross-sectional data. However, it can often times be challenging to justify the assumption of common regression coefficients across all respondents. This manuscript presents a heterogeneous Bayesian regression model that enables the estimation of individual-level-regression coefficients in cross-sectional data involving a single observation per response unit. A Gibbs sampling algorithm is developed to implement the proposed Bayesian methodology. A Monte Carlo simulation study is constructed to assess the performance of the proposed methodology across a number of experimental factors. We then apply the proposed method to analyze data collected from a consumer psychology study that examines the differential importance of price and quality in determining perceived value evaluations.