Nonparametric and Semiparametric Inference in Econometric   Models
{'Code': '04050100', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'Daniel H. Newlon', 'PO_EMAI': None, 'PO_PHON': None}
This project develops nonparametric and semiparametric methods for              estimation of econometric models.  The research consists of three main          parts: 1) additive interactive regression, 2) covariance matrix                 estimation in the presence of heteroskedasticity and autocorrelation            of unknown forms, and 3) general methods for inference in                       semiparametric models.                                                                                                                                          Additive Interactive Regression (AIR) models are econometric models             which put more structure on the regression function than nonparametric          models, and thus circumvent the problems of dimensionality that                 severely hinder the use of fully nonparametric techniques in applied            econometrics.  This project adds to that line of work by analyzing the          statistical properties of AIR models, and developing efficient                  estimation techniques for them.                                                                                                                                 The work on estimation of the covariance matric under varying                   distributional assumptions extends existing work on comparing                   different kernel estimators by determining suitable bandwidth/lag               truncation parameters for use with these estimators, and comparing the          properties of these estimators with those of other covariance                   estimators.  In addition, computationally efficient estimation                  techniques are developed for obtaining consistency against arbitrary            forms of heteroskedasticity.                                                    %%%                                                                             This project investigates the properties of various estimation                  techniques used in econometric models.  Such models are used widely in          all areas of applied economics, including macroeconomics and                    government policy analysis, studies of the behavior of firms and                industries, and the dynamics of the international economy.  Until               recently, all these types of econometric investigations started by              postulating a mathematical function or system of functions simulating           the economic relationships to be examined.  Research has shown that in          many cases the results of these studies can be dependent on the                 particular mathematical specification of the functions.                                                                                                         During the last decade much econometric work has focused on developing          statistical estimation techniques which do not require a specified              mathematical function.  These techniques, known generically as                  nonparametric techniques, have obvious advantages over the                      nonparametric ones in that fewer assumptions about the structure of             the economy have to be made, and more information about the economy is          derived from the data themselves.  However, statistical problems arise          with the use of nonparametric techniques.   This project adds                   significantly to the existing work on nonparametric estimation by               examining three of the most difficult problems, namely dimensionality,          covariance estimation, and computational efficiency.
{'FirstName': 'Donald', 'LastName': 'Andrews', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Donald Andrews', 'EmailAddress': 'donald.andrews@yale.edu', 'NSF_ID': '000463266', 'StartDate': '02/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Yale University', 'CityName': 'NEW HAVEN', 'ZipCode': '065113572', 'PhoneNumber': '2037854689', 'StreetAddress': '150 MUNSON ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Connecticut', 'StateCode': 'CT', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'CT03', 'ORG_UEI_NUM': 'FL6GV84CKN57', 'ORG_LGL_BUS_NAME': 'YALE UNIV', 'ORG_PRNT_UEI_NUM': 'FL6GV84CKN57'}
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{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821021.xml'}