A Bayesian Cross-Validated Likelihood Method for Comparing Alternative Specifications of Quantitative Models

There are many situations in marketing in which several alternative quantitative models may be built to model a particular marketing phenomenon or system. Few methods exist for comparing the fit of such models if the models are not nested, especially if their performance on each of several criteria is important. This paper proposes a Bayesian cross-validated likelihood (BCVL) method for comparing quantitative models. It can be used when the models are either nested or nonnested, and is especially useful for nonnested models. A simulation based upon a typical marketing modeling situation shows the incremental benefit of using the BCVL method rather than existing techniques, and explores the circumstances under which BCVL works best. The applicability of the BCVL method is demonstrated using several typical marketing modeling situations.