Research in Econometric Theory
{'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 research addresses three important topics in applied econometrics,         namely analysis of panel data, stability of time series data, and               derivation of instrumental variables.  Panel data, that is a set of             data including both time series and cross sectional observations have           become widely used in studies of consumer behavior, entrance into and           exit from the labor market, and decision-making within the family.              Time series data play a very important role in many analyses of the             long term performance of an economy and the influence of government             policy.  Instrumental variables often come into econometric research            when the variables required in a model are either not observable, or            have statistical properties which interfere with the model's                    applicability.                                                                                                                                                  The work on estimation of models with panel data extends the existing           literature on linear models to nonlinear models.  In particular the             logit and probit models are extended to include time varying and                random parameters.  Dynamic models with lagged dependent variables are          also derived and analyzed.  The research on time series analysis                involves the derivation of tests for unit roots.  The tests include             allowance for autocorrelation in the variables.  The power and                  robustness of the tests under varying assumptions concerning trend and          stationarity are assessed.  The work on instrumental variable                   estimators provides a simple and easily applicable method of improving          the efficiency of estimators, and brings under one general principle            the treatments of diverse estimators such as 3-Stage Least Squares and          Full Information Maximum Likelihood.                                            %%%                                                                             This project bridges an important gap between theoretical and applied           econometrics.  It focuses on three important topics widely used in              econometric research, namely analysis of panel data, stability of time          series, and the derivation of instrumental variables.                                                                                                           The term "panel data" refers to any data set of observations on a               given group of individuals, firms, or organizations taken over time.            In this respect a panel data set is comprised of both cross                     sectional data (on the different individuals) and time series data              (observations on the same group of individuals taken at different               times).  Such data are widely used in economic analysis of the                  labor market, consumer behavior, and economic growth.                                                                                                           Time series data are very prevalent in studies of the aggregate                 economy.  This project provides tests for the stability of a given set          of time series data, and examines the robustness and power of those             tests under various assumptions about the correlation among the                 observations and the tendency of the entire series to change through            time.                                                                                                                                                           Instrumental variables are often used in economic models in place of            more relevant variables in order to bring desired statistical                   properties to the model.  For instance, in many models the relevant             variables are not observed, but instruments derived from them can be            observed.  This project provides efficient methods for deriving                 instrumental variables and applies them to several estimation                   techniques.
{'FirstName': 'Peter', 'LastName': 'Schmidt', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Peter Schmidt', 'EmailAddress': 'schmidtp@pilot.msu.edu', 'NSF_ID': '000335077', 'StartDate': '04/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821154.xml'}