摘要：In the absence of reliable a priori information, choosing the appropriate theoretical model to describe an industry’s behavior is a critical issue for empirical studies about market power. A wrong choice may result in model misspecification and the conclusions of the empirical analysis may be driven by the wrong assumption about the behavioral model. This paper develops a methodology aimed to reduce the risk of misspecification bias. The approach is based on the 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 nonparametric characterization of the function linking these variables to price. This information may be used to guide 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 its data driven approach that does not rely on prior knowledge of the industry. The method reverses the usual hypothesis testing approach. Instead of first choosing the model based on a priori information and then testing if 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 information about the behavioral model, or little confidence in the information that is available.
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