Nonparametric and Semiparametric Econometrics Using Artifical Neural Networks

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Using methods recently developed in econometrics and mathematical statistics, this project will investigate the theory of "learning" in a class of "artificial neural network" models. Models are to be developed that will yield potentially more efficient and powerful estimation and testing procedures. The research falls into four interrelated ares: large-sample properties of nonparametric estimation procedures based on neural network models, relative computational properties of different methods for optimizing the network learning (estimation) objective function, application of nonparametric network-based procedures to construct semi-parametric estimators and perform specification tests, and comparison of network-based procedures to standard nonparametric and semi-parametric procedures based on kernel estimators and series estimators, among others. The techniques developed in this field have been successfully used to forecast in engineering and technology. Some specific applications have included various industrial settings and in defense to accomplish tasks that had been insoluble. Some examples include bomb detection in airports, distinguishing between sonar signals generated from rocks and mines, vibration damping in machinery, handwriting character recognition and robot coordination and control. Further work in this area should continue to expand the fields of applications beyond economics.