Nonparametric Estimation of Average Derivatives
{'Code': '04050100', 'Directorate': {'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'jerry blackmon'}
An important movement in econometrics in the l980's has been the                development of estimation techniques that do not rely heavily on                restrictive functional form assumptions of traditional                          econometric models.  The aim of such techniques is to permit the                economic effects of variables to be measured in ways that do not                require ad hoc assumptions on how individual agents (or separate                data observations) vary in unobserved dimensions.  Parametric                   models explicitly or implicitly utilize such assumptions, which                 can lead to unknown and unmeasurable biases in parameter values                 and precision measures.                                                                                                                                         The purpose of the proposed research is to create and study fully               implementable nonparametric estimators of average derivatives.                  The statistical properties of the proposed estimators will be                   established, and their practical performance will be thoroughly                 analyzed via Monte Carlo simulation.  The aim is to create a new                empirical tool for the study of economic relationships, which                   does not rely on restrictive modelling assumptions, and to                      establish its practical value by examining its performance.
{'FirstName': 'Thomas', 'LastName': 'Stoker', 'EmailAddress': 'tstoker@sloan.mit.edu', 'StartDate': '04/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Massachusetts Institute of Technology', 'CityName': 'Cambridge', 'ZipCode': '021394301', 'PhoneNumber': '6172531000', 'StreetAddress': '77 MASSACHUSETTS AVE', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA'}
{'Code': '1320', 'Text': 'ECONOMICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8721889.xml'}