Publications

Detailed Information

Flexible Heterogeneous Utility Curves: A Bayesian Spline Approach

Cited 13 time in Web of Science Cited 17 time in Scopus
Authors

Kim, Jin Gyo; Menzefricke, Ulrich; M. Feinberg, Fred

Issue Date
2007
Publisher
INFORMS (Institute for Operations Research and Management Sciences)
Citation
Management Science, 53, 340-354
Keywords
choice modelsutility theoryheterogeneitysplinesBayesian methodsMarkov chain Monte Carlo
Abstract
Empirical evidence suggests that decision makers often weight successive additional units of a valued
attribute or monetary endowment unequally, so that their utility functions are intrinsically nonlinear or irregularly
shaped. Although the analyst may impose various functional specifications exogenously, this approach is
ad hoc, tedious, and reliant on various metrics to decide which specification is best. In this paper, we develop
a method that yields individual-level, flexibly shaped utility functions for use in choice models. This flexibility
at the individual level is accomplished through splines of the truncated power basis type in a general additive
regression framework for latent utility. Because the number and location of spline knots are unknown, we use
the birth-death process of Denison et al. (1998) and Greens (1995) reversible jump method. We further show
how exogenous constraints suggested by theory, such as monotonicity of price response, can be accommodated.
Our formulation is particularly suited to estimating reaction to pricing, where individual-level monotonicity
is justified theoretically and empirically, but linearity is typically not. The method is illustrated in a conjoint
application in which all covariates are splined simultaneously and in three panel data sets, each of which has
a single price spline. Empirical results indicate that piecewise linear splines with a modest number of knots fit
these data well, substantially better than heterogeneous linear and log-linear a priori specifications. In terms
of price response specifically, we find that although aggregate market-level curves can be nearly linear or loglinear,
individuals often deviate widely from either. Using splines, hold-out prediction improvement over the
standard heterogeneous probit model ranges from 6% to 14% in the scanner applications and exceeds 20% in
the conjoint study. Moreover, optimal profiles in conjoint and aggregate price response curves in the scanner
applications can differ markedly under the standard and the spline-based models.
ISSN
0025-1909 (Print)
1526-5501 (Online)
Language
English
URI
https://hdl.handle.net/10371/67972
DOI
https://doi.org/10.1287/mnsc.1060.0616
Files in This Item:
Appears in Collections:

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share