Journal of Marketing Research 2005
Structural Modeling and Policy Simulation
Abstract
A primary goal of research in marketing is to evaluate and recommend optimal policies for marketing actions, or “instruments” in the terminology of Franses (2005). In this respect, marketing is a very policy-oriented field, and it is ironic that so much published research skirts the issue of policy evaluation. Franses’s article draws much needed attention to the question of what sort of model is usable for policy simulation and evaluation. Our perspective on what constitutes a valid model for policy evaluation differs from Franses’s view, but we believe our view complements his in many important respects. We also strongly believe that marketing has much to contribute to the literature on structural modeling. We outline some of what we believe are the advantages for marketing scholars of using structural modeling for policy evaluations and some of the challenges presented by marketing problems. Franses focuses on a reduced-form sales response model in which the outcome variable (yt) is modeled conditional on marketing variables (xt). If customers anticipate future marketing actions and take these into account in responding to the environment at time t, an additional equation is appended to the system to describe the evolution of the xt variables. In Franses’s view, this system can be used for policy simulation if both the y and x equations have timeinvariant parameters. That is, the Lucas critique, which implies that parameters of reduced-form models change if the policy regime changes, does not apply. According to Franses, a model must pass standard diagnostics, possess good predictive properties, and exhibit parameter stability to be useful for policy simulation. We applaud the attention Franses is bringing to model diagnostics. We believe that structural work in both marketing and economics should pay close attention to the central features of the data. Increased use of model diagnostics will help ensure that structural models are capable of capturing these features. However, we do not believe that all the criteria proposed by Franses, such as out-of-sample validity and parameter stability, are either necessary or sufficient to render a model useful for policy simulation. Reduced-form models can pass all diagnostics, including out-of-sample validation, and still provide misleading predictions about the effects of policy changes. Reduced-form
- DOI
- 10.1509/jmkr.42.1.22.56887
- Sources
- openalex