Toward Accurate Inference in Nonlinear Econometrics
{'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 award is for continued support for a program of research focusing          on nonlinear, econometric methods.  The research is based on the                premise that an econometric specification is an approximation to an             underlying data generating mechanism.  In keeping with this                     perspective, methodologies will be developed that improve the                   approximation as more information becomes available and permits more            reliable inference at each intermediate stage of model evolution.  The          scientific importance of this research derives from the fact that the           goal of empirical research is to make an inference about an economic            proposition per se, not about an economic proposition entangled with a          potentially misspecified or incorrect model.  With standard approaches          to specification tests, one cannot be sure that the estimate associated         with an inference is accurate.  The objective of this research can be           viewed as the development of a model selection strategy that guarantees         that the final estimate is accurate.                                                                                                                            This objective is to be accomplished at a level of generality that              encompasses most nonlinear, econometric inference procedures.  The              basic idea is to endow a procedure with nonparametric properties by             replacing the structural model with a truncated series expansion, or            the error density with a truncated expansion, or both.  By letting the          truncation point grow adaptively with sample size, ultimate convergence         to the underlying data generating mechanism is assured.  Applications           to conditionally heterogeneous time-series such as occur in finance and         monetary and macro economics will be undertaken.  Bayesian methods will         also be investigated.
{'FirstName': 'A. Ronald', 'LastName': 'Gallant', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'A. Ronald Gallant', 'EmailAddress': 'aronaldg@gmail.com', 'NSF_ID': '000460766', 'StartDate': '10/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'North Carolina State University', 'CityName': 'RALEIGH', 'ZipCode': '276950001', 'PhoneNumber': '9195152444', 'StreetAddress': '2601 WOLF VILLAGE WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'NC02', 'ORG_UEI_NUM': 'U3NVH931QJJ3', 'ORG_LGL_BUS_NAME': 'NORTH CAROLINA STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8808015.xml'}