Some Issue on Modelling Unobservable Variables
{'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}
Models based on variables which are inherently unobservable are found           very often in economics, psychology, and other social sciences.  They           might arise from errors in the data being analyzed, or because the              behavioral responses of agents are in part determined by                        characteristics which cannot be observed or measured.  Obtaining                meaningful insight from models in which unobservable variables play a           role requires either expanding the data set with more precise                   measurement or developing an analytical framework to extract the                relevant information on the unobserved variables from the existing              data.  The former is often too costly or even impossible, particularly          when large Federal statistical data sets are being studied.                                                                                                     The project examines two analytical models widely used in econometric           studies using Federal data, namely the errors-in-variables model and            the hedonic model.  The errors-in-variables model is studied from the           standpoint of various assumptions regarding the structure of the                errors, and model identification, and statistical inference.  Using             the hedonic model this project establishes correspondences between the          underlying characteristics and the observed variables.  It also                 derives conditions regarding identification, estimation, and                    interpretation of the coefficients.                                             %%%                                                                             Models based on variables which are inherently unobservable are found           very often in economics, psychology, and other social sciences.  They           might arise from errors in the data being analyzed, or because the              behavioral responses of agents are in part determined by                        characteristics which cannot be observed or measured.  Large Federal            data sets, such as those collected by the Bureau of the Census or the           Bureau of Labor Statistics, often contain grouped data.  For reasons            of confidentiality or ease of collection, a household might be asked            to list its income range, for instance between $15,000 and $25,000,             rather than its exact income.  If an econometric analysis uses these            data on the assumption that they are precise measurements, the results          can be misleading or erroneous.                                                                                                                                 Obtaining meaningful insight from models in which unobservable                  variables play a role requires either expanding the data set with more          precise measurement or developing an analytical framework to extract            the relevant information on the unobserved variables from the existing          data.  The former approach is more desirable, but often too costly,             particularly when a researcher is using large Federal statistical data          sets.  This project focuses on the latter, more analytical strategy.            It develops an econometric framework to enhance the reliablity and              usefulness of research based on Federal data where latent variable              models are appropriate.
{'FirstName': 'Cheng', 'LastName': 'Hsiao', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Cheng Hsiao', 'EmailAddress': 'chsiao@usc.edu', 'NSF_ID': '000465292', 'StartDate': '03/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Southern California', 'CityName': 'LOS ANGELES', 'ZipCode': '90033', 'PhoneNumber': '2137407762', 'StreetAddress': '3720 S FLOWER ST FL 3', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '34', 'CONGRESS_DISTRICT_ORG': 'CA34', 'ORG_UEI_NUM': 'G88KLJR3KYT5', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF SOUTHERN CALIFORNIA', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '132000', 'Text': 'Economics'}, {'Code': '133300', 'Text': 'Methodology, Measuremt & Stats'}, {'Code': '914500', 'Text': 'SPECIAL PROGRAMS-RESERVE'}]
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