Estimating Long Run Economic Equilibrium
{'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 project focuses on three important topics in econometric                   analysis, namely 1) the estimation of long run economic equilibrium,            2) the dynamics of models which are not fully identified, and 3) the            unification of econometric distribution theory.                                                                                                                 The first topic involves examining the properties of economic time              series data, in particular joint dependency of time series data sets,           and nonstationarity.  This project serves to link the econometric               methodologies of cointegration models, vector-autoregression                    techniques, and error correction models into a unified framework.  In           doing so it derives an important relationship between correlation               among time series variables and economic equilibrium.                                                                                                           The work on partially identified models concentrates on two models in           which identification is particularly important, namely simultaneous             equations models, and spurious regressions in time series.                      Simultaneous equations models are analyzed in detail via systems                methods of estimation.  The project will produce a complete theory              for estimation systems in which some equations are not fully                    identified or in which components of equations are not identified.                                                                                              The third part of the project applies operational algebra, a recently           developed matrix calculus, to distributional problems in econometrics           and statistics.  This approach avoids much of the complex polynomial            algebra that accompanies analysis of multivariate distribution theory.          In particular, it obviates the need for expansion series                        representation of probability densities, and in turn greatly                    facilitates the derivation of both asymptotic properties and finite             sample properties of estimators.                                                %%%                                                                             This research project is an analysis of the relationship between the            statistical properties exhibited by many sets of economic time series           data and the concept of econommic equilibrium.  At the heart of most            economic theory is the notion that systems tend to move toward a point          of stability, rather than fluctuating randomly.  From an empirical              standpoint, however, the isolation of equilibrium points in actual              data has proven to be very difficult.  This is particularly true for            time series data.  When more than one series of data enter a model,             for instance income data and price data, deriving an economic                   interpretation to the results of econometric analysis becomes                   problematic, since the values in both set of data exhibit the same              tendency to grow over time.                                                                                                                                     By applying recently developed statistical techniques this research             isolates long run equilibrium in economic time series data.  Tests are          developed for the stability of such equilibruim points.  Other                  econometric issues such as the identification of models consisting of           simultaneous equations and the derivation of asymptotic properties of           estimators are also addressed.
{'FirstName': 'Peter', 'LastName': 'Phillips', 'PI_MID_INIT': 'C', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Peter C Phillips', 'EmailAddress': 'peter.phillips@yale.edu', 'NSF_ID': '000114963', 'StartDate': '04/01/1989', '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'}
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{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821180.xml'}