Integrative Noise and Fixed Point Methods as Solution       Techniques for Generalized Linear-Quadratic Models of       Endogenous Information
{'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}
Rational expectations models of macroeconomics that incorporate                 asymmetric information typically use a linear-quadratic technical               framework because individuals' predictions or decisions are then                linear (and hence tractable) functions of observable variables.                 If individuals have asymmetric information and obtain information               by observing endogenous variables -- prices, aggregate output or                the like -- then it becomes technically difficult to solve such                 models for the endogenous aggregates.  The standard method, the                 method of undetermined coefficients, is limited to special cases.                                                                                               The investigator developed two promising new solution methods and               in this project he expands them to a more general setting.  He                  will show that all linear rational expectations models with                     asymmetric information are expressible as special cases of a                    general structure in which economic, informational, and                         statistical structure is captured by specifying the dimensions                  and elements of matrices of analytic functions.  Any economic                   structure can be rigorously modelled and empirically estimated by               specifying the details of these matrices.  Solving this model                   requires factoring the matrix.  The first approach designs the                  model so that the matrix is comprised solely of exogenous                       elements -- which are easily factored -- and then perturbing the                model to achieve the solution of the economically interesting                   model.  The second approach uses a contraction mapping fixed                    point method.                                                                                                                                                   The model will integrate the informational externality micro                    literature with the rational expectations literature.  This paves               the way for further analysis, particularly of the welfare                       characteristics of alternative policy regimes, using what we know               from the micro literature.  The investigator has already shown                  how informational externalities lead to aggregate fluctuations                  that mimic business cycles for special cases of stochastic                      driving processes.  This project investigates how the                           persistence, amplitudes and co-movements of economic aggregates                 are affected by such externalities when the driving processes are               more general.
{'FirstName': 'Bart', 'LastName': 'Taub', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Bart Taub', 'EmailAddress': 'b-taub@uiuc.edu', 'NSF_ID': '000107606', 'StartDate': '04/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Virginia Polytechnic Institute and State University', 'CityName': 'BLACKSBURG', 'ZipCode': '240603359', 'PhoneNumber': '5402315281', 'StreetAddress': '300 TURNER ST NW', 'StreetAddress2': 'STE 4200', 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '09', 'CONGRESS_DISTRICT_ORG': 'VA09', 'ORG_UEI_NUM': 'QDE5UHE5XD16', 'ORG_LGL_BUS_NAME': 'VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'M515A1DKXAN8'}
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{'Code': '1320', 'Text': 'Economics'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8721304.xml'}