Estimation: Inference and Model Selection for Neural Network Models in Econometrics

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Cognitive scientists have recently developed a rich and interesting class of nonlinear models inspired by the neural architecture of the brain (neural network models). These networks are capable of learning through interaction with their environment, in a process which can be viewed as a recursive statistical estimation procedure. The promise of these models and associated estimation procedures and the excitement evident across a spectrum of disciplines including psychology, computer science, genetics, linguistics and engineering is founded on the demonstrated success of neural network modeling in solving a diverse range of difficult problems. Especially impressive have been solutions to problems which had previously resisted conventional attempts at solution, as well as relatively quick and reliable solutions to problems which had previously yielded comparable effective solutions grudgingly, and after several man- years of more conventional effort. The objectives of this project are (1) to investigate the applicability of neural network models to the study of economic phenomena and to refine and extend these models in directions suitable to the study of economic phenomena, (2) to refine and extend the learning methods (estimation procedures) used to train the networks so as to obtain parameter estimates which converge quickly and reliably when faced with economic data, and (3) to apply model specification and selection techniques developed by the investigator in previous funded research to neural network models in order to develop techniques for choosing between competing neural network architectures for particular problems. This is an exciting project because no one has ever applied neural network models to economics. These new methods will dramatically reduce the computational time needed to solve complex economic problems. The neural network models will provide a new methodology for studying the way economic agents learn from their environment. Neural networks appear to be particularly well suited to nonlinear economic forecasting, so these new methods could provide us with better predictions of the economic future.