Modelling Pricing Behavior with Weak A‐Priori Information: Exploratory Approach

In the absence of reliable a priori information, choosing the appropriate theoretical model to describe anindustry’s behavior is a critical issue for empirical studies about market power. A wrong choice may result inmodel misspecification and the conclusions of the empirical analysis may be driven by the wrong assumptionabout the behavioral model.This paper develops a methodology aimed to reduce the risk of misspecification bias. The approach is based onthe sequential application of a sliced inverse regression (SIR) and a nonparametric Nadaraya‐Watson regression(NW). The SIR‐NW algorithm identifies the factors affecting pricing behavior in an industry and provides a nonparametriccharacterization of the function linking these variables to price. This information may be used toguide the choice of the model specification for a parametric estimation of market power.The SIR‐NW algorithm is designed to complement the estimation of structural models of market behavior,rather than to replace it. The value of this methodology for empirical industrial organization studies lies in itsdata‐driven approach that does not rely on prior knowledge of the industry. The method reverses the usualhypothesis‐testing approach. Instead of first choosing the model based on a priori information and then testingif it is compatible with the data, the econometrician selects a theoretical model based on the observed data.Thus, the methodology is particularly suited for those cases where the researcher has no a priori informationabout the behavioral model, or little confidence in the information that is available .