Estimation: Inference and Model Selection for Neural        Network Models in Econometrics
{'Code': '04050100', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'Lynn A. Pollnow', 'PO_EMAI': None, 'PO_PHON': None}
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.
{'FirstName': 'Halbert', 'LastName': 'White', 'PI_MID_INIT': 'L', 'PI_SUFX_NAME': 'Jr.', 'PI_FULL_NAME': 'Halbert L White', 'EmailAddress': 'hwhite@ucsd.edu', 'NSF_ID': '000393986', 'StartDate': '08/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-San Diego', 'CityName': 'LA JOLLA', 'ZipCode': '920930021', 'PhoneNumber': '8585344896', 'StreetAddress': '9500 GILMAN DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '50', 'CONGRESS_DISTRICT_ORG': 'CA50', 'ORG_UEI_NUM': 'UYTTZT6G9DT1', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, SAN DIEGO', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8806990.xml'}