Semiparametric Estimation of Multivariate Latent Variable   Models
{'Code': '04050100', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'Robin A. Cantor', 'PO_EMAI': None, 'PO_PHON': None}
The primary object of this research is the investigation of an                  innovative technique for estimating a class of statistical models               widely used in economics and other social sciences. The general model           with which the line of work is concerned is one where the dependent             variable is not continuous.  Such models include those in which data            censoring or aggregation results in the dependent variable being a              discrete grouping or ordering of an underlying but unobserved                   continuous variable.  Another such model is the so-called discrete              choice model, which is used to analyze many economic decisions, such            as housing tenure choice, mode of transportation, and the purchase of           consumer durables. In these models the observed data usually consists           simply of an observation as to whether a particular choice was made or          not.  All these types of models are fundamentally based on the idea             that an underlying continuous variable, often called a latent                   variable, is really the crucial factor in the person's making a                 particular decision, but that variable is not itself observed.  What            is observed is an index or indicator which merely shows the choice              made by the economic agent.  In doing such analysis one must make               assumptions about the  underlying variable, and in particular about             its distributional form, and past work has shown research results to            be quite dependent on the distributional assumptions.  Professor                Powell is a leader in a line of econometric research which develops             estimation techniques robust to distributional assumptions.                                                                                                     Technically, Professor Powell in this project derives semiparametric            estimators for analyzing latent variable models.  The procedure                 consists of two steps.  First, an index is derived based on a group of          variables exogenous to the model.  Then for each observation in the             data the conditional distribution of the latent variable is centered            on this index.  The second step uses the newly created data set of              indices to estimate the parameters in which the researcher is                   interested.
{'FirstName': 'James', 'LastName': 'Powell', 'PI_MID_INIT': 'L', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'James L Powell', 'EmailAddress': 'powell@econ.berkeley.edu', 'NSF_ID': '000456695', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Wisconsin-Madison', 'CityName': 'MADISON', 'ZipCode': '537151218', 'PhoneNumber': '6082623822', 'StreetAddress': '21 North Park Street', 'StreetAddress2': 'Suite 6301', 'CountryName': 'United States', 'StateName': 'Wisconsin', 'StateCode': 'WI', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'WI02', 'ORG_DUNS_NUM': '161202122', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF WISCONSIN SYSTEM', 'ORG_PRNT_DUNS_NUM': '041188822'}
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{'Code': '1320', 'Text': 'Economics'}
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