Nonlinear Models with Individual Effects
{'Code': '04050100', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'jerry blackmon', 'PO_EMAI': None, 'PO_PHON': None}
Panel data, consisting of observations across time for individual               economic agents, allows the possibility of controlling for                      unobserved individual heterogeneity.  Such heterogeneity can                    arise from unobserved differences in tastes or technology and can               be an important phenomenon.  In such instances failure to control               for it can result in misleading inferences.  This problem is                    particularly severe when the unobserved heterogeneity affects                   explanatory variables.  Situations of this sort arise naturally                 when some of the explanatory variables are decision variables, as               is the case, for example, in dynamic panel data models.                                                                                                         Models and methods of controlling for unobserved heterogeneity in               linear models are well established.  However, less is known about               how to control for unobserved heterogeneity in nonlinear models.                The methods that work for linear models do not carry over in a                  straightforward way to most nonlinear models.  Except in special                circumstances, it is not possible to eliminate individual effects               by some data transformation, such as first differencing of time                 series observations which works in linear models.  Also, fixed                  effects estimators of nonlinear models, which treat the                         individual effects as parameters to be estimated, encounter                     problems because of the short length of the typical time series                 in economic panel data.                                                                                                                                         This research concerns two topics in controlling for unobserved                 heterogeneity in nonlinear models of panel data.  The first topic               deals with modelling the distribution of the individual effects,                conditional on the explanatory variables.  Such models can be                   used to specify random effects limited dependent variable models                with a nonparametric conditional mean for the individual effect.                The second topic investigates the use of the jacknife, applied to               the time series dimension of panel data, for reduction of the                   bias of fixed effects estimators in nonlinear models.  The                      research will examine the extent to which this nonparametric bias               reduction method can alleviate the incidental parameters bias, in               different models.  The basic goals of the project are to assess                 the potential empirical impact of the models and methods to be                  considered, as well as derivation of asymptotic distributions for               the various estimators.
{'FirstName': 'Whitney', 'LastName': 'Newey', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Whitney K Newey', 'EmailAddress': 'wnewey@mit.edu', 'NSF_ID': '000424668', 'StartDate': '07/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Princeton University', 'CityName': 'PRINCETON', 'ZipCode': '085442001', 'PhoneNumber': '6092583090', 'StreetAddress': '1 NASSAU HALL', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New Jersey', 'StateCode': 'NJ', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'NJ12', 'ORG_UEI_NUM': 'NJ1YPQXQG7U5', 'ORG_LGL_BUS_NAME': 'THE TRUSTEES OF PRINCETON 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': '8810049.xml'}