Stochastic Economic Variables Having Equilibria as Simple   Attractors
{'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}
This project makes an important contribution to the statistical theory          concerning the analysis of economic time series data.  Traditionally,           models considered by time series analysts have concentrated on a few            important economic variables and incorporated complicated lag                   structures of those variables.  Structure of the economy was inferred           from the data by examining the way the economy evolved and changed              through time.  This is in contrast to another form of economic                  modelling, which includes many variables in a sharply specified                 mathematical structure consisting of many equations.  Both techniques           have their advantages and disadvantages, and this project is part of            an ongoing attempt to meld the two modelling strategies into a single           coherent, and statistically consistent framework.                                                                                                               Much empirical research in macroeconomics has shown that economic time          series data typically include non-stationary processes such as trends.          Different data series, for example national income data, GNP data, and          price series, might include a common trend or tendency to grow over             time.  Thus these data are statistically linked with each other in the          long run.  To gain any meaningful insight into the actual movements of          the variables in the economy, one must purge the data of the influence          of non-stationary processes.  In the past, achieving stationarity has           been especially difficult if an analytical model included several data          series each containing similar growth tendencies.  Professor Granger            has developed a statistical characterization of time series known as            co-integration in which a linear combination of data series is itself           stationary.                                                                                                                                                     This project makes a significant contribution to the existing                   literature in that it extends the idea of co-integration of time                series to allow for nonlinear common trends, and offers an economic             interpretation of data fluctuations as movements away from economic             equilibrium.  For example the price of an agricultural product such as          tomatoes will vary from one section of the country to another, but              will not vary a great deal, at least not in the long run.  If the               price of a product differs greatly from one location to another,                market forces will act to eliminate the price disparities and reach an          equilibrium.  Professor Granger empirically identifies the equilibrium          concept as a point to which the data in the series seem to gravitate.           This point is known as an attractor, and the task of the                        econometrician is to isolate possible attractors in a group of data             sets that the economic theory has suggested should be considered in             the analysis and to confirm their importance.  This work significantly          increases the strength of the bond between economic theory and                  empirical analysis.
{'FirstName': 'Clive', 'LastName': 'Granger', 'PI_MID_INIT': 'W', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Clive W Granger', 'EmailAddress': 'cgranger@weber.ucsd.edu', 'NSF_ID': '000189246', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-San Diego', 'CityName': 'LA JOLLA', 'ZipCode': '920930021', 'PhoneNumber': '8585344896', 'StreetAddress': '9500 GILMAN DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '50', 'CONGRESS_DISTRICT_ORG': 'CA50', 'ORG_UEI_NUM': 'UYTTZT6G9DT1', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, SAN DIEGO', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8704669.xml'}