Monotonic regression based on Bayesian P-splines: an application to estimating price response functions from store-level scanner data

Brezger, Andreas and Steiner, Winfried J. (2003): Monotonic regression based on Bayesian P-splines: an application to estimating price response functions from store-level scanner data. Collaborative Research Center 386, Discussion Paper 331

Abstract

Generalized additive models have become a widely used instrument for flexible regression analysis. In many practical situations, however, it is desirable to restrict the flexibility of nonparametric estimation in order to accommodate a presumed monotonic relationship between a covariate and the response variable. For example, consumers usually will buy less of a brand if its price increases, and therefore one expects a brand's unit sales to be a decreasing function in own price. We follow a Bayesian approach using penalized B-splines and incorporate the assumption of monotonicity in a natural way by an appropriate specification of the respective prior distributions. We illustrate the methodology in an empirical application modeling demand for a brand of orange juice and show that imposing monotonicity constraints for own- and cross-item price effects improves the predictive validity of the estimated sales response function considerably.

• Item Type: Paper (Research Paper)
• Keywords: Generalized Additive Model, Markov Chain Monte Carlo, Sales Promotion, Own- and Cross-Item Price Effects, Asymmetric Quality Tier Competition
• Subjects: Mathematics, Computer Science and Statistics; Mathematics, Computer Science and Statistics > Statistics; Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386
• Dewey Classification: 600 Natural sciences and mathematics; 600 Natural sciences and mathematics > 510 Mathematics
• Language: English