Data Mining for Theorists
Among those interested in statistically testing formal models,two approaches dominate. The structural estimation approach derives a structural probability model based on the formal model and then estimates parameters associated with that model. The reduced-form approach generally applies off-the-shelf techniques---such as OLS, logit, or probit---to test whether the independent variables are related to a decision variable according to the comparative statics predictions. We provide a new statistical method for the comparative statics approach. The decision variable of interest is modeled as a polynomial function of the available covariates, which allows for the nonmonotonic and interactive relationships commonly found in strategic choice data. We use the adaptive lasso to reduce the number of parameters and prevent overfitting, and we obtain measures of uncertainty via the nonparametric bootstrap. The method is "data mining" because the aim is to discover complex relationships in data without imposing a particular structure,but "for theorists" in that it was developed specifically to deal with the peculiar features of data on strategic choice. Using a Monte Carlo simulation, we show that the method handily outperforms other non-structural techniques in estimating a nonmonotonic relationship from strategic choice data.
empirical implications of theoretical models
functional form misspecification
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