Investigating Alternative Estimation Methods for LDV        Econometric Models that Normally Involve Multidimensional   Integrals
{'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}
The estimation of a large class of limited dependent variables             (LDV) models, though possessing desirable theoretical properties, is            infeasible by conventional methods because it requires the evaluation           of multidimensional integrals.  Examples are discrete choice models             with several contemporaneously correlated alternatives and LDV models           with temporally correlated errors.                                                   A recently proposed method (McFadden (1986)), the Method of                Simulated Moments (MSM), was shown to make the estimation of certain            discrete choice models feasible, by avoiding the need for numerically           calculating multiple integrals.  Hajivassiliou and McFadden (1987)              extended the method and presented limited computational experiments on          it.                                                                                  With current mainframe computer technology, however, it is not             possible to investigate the performance of this novel method (MSM)              against the theoretical benchmark of classical maximum likelihood               estimation (MLE) which requires the intractable integrals.  Given the           nature of these estimation problems, the application of supercomputer           vectorization techniques will afford a comprehensive set of experiments         to compare the MSM against the other classical estimation methods.              Moreover, the use of a supercomputer will make feasible the classical           method of MLE for several of these models, which, in view of the                integrals involved, is currently beyond the capacity of mainframe               computers.  This award will provide access to the requisite computing           capacity at the Pittsburgh Center.
{'FirstName': 'Vassilis', 'LastName': 'Hajivassiliou', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Vassilis A Hajivassiliou', 'EmailAddress': None, 'NSF_ID': '000114220', 'StartDate': '10/15/1987', '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'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8711656.xml'}