Nonparametric Methods for Economic Models

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9410182 Matzkin Most existing tests of economic models proceed by first, specifying the functions in the model up to some parameters. Next, the parameters are estimated, and a test for the restrictions that the model implies on the values of the estimated parameters is performed. This procedure suffers typically from two drawbacks. First, if the restrictions are rejected, it is not clear whether this is due to a wrong model or a wrong parametric specification. And second, if the restrictions are not rejected, it is not clear whether this implies any strong evidence in favor of the model, since the restrictions were only necessary and not sufficient. This project addresses these problems by developing new methods for the identification, estimation, and testing of nonparametric models. First, the project will determine how several types of restrictions, such as inequality restrictions and optimizing behavior, can be used to guarantee the identification of functions in systems of nonparameteric simultaneous equations. The systems that will be considered include those with unobservable endogenous variables, discrete variables, and nondifferentiable functions. The results of this research will be useful to econometricians interested in estimating systems of equations that are either nonparametric or nonlinear in parameters. Second, the project develops nonparametric restrictions characterizing the behavior of observable variables in economic models. The results will be useful to falsify specific theories using observable behavior. Finally, the project develops estimators for nonparametric functions in systems of equations using weighted average methods and shape- restricted methods.