SNP Models for Economic Time Series:  Theory, Methods, and  Applications
{'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}
Modeling and estimation of economic time series primarily concern               making inferences about the intertemporal law of motion of                      economic variables.  The general objective of this project is to                refine and apply the seminonparametric approach (SNP) for                       estimation of the underlying law of motion of a stationary                      process.  The SNP approach was initially developed by Tauchen and               Gallant for an asset pricing application.  It can accomodate just               about any arbitrary deviations from linearity and normality and,                unlike other methods,  provides a compact summary of the data in                the form of a fitted parametric density which can be used for the               purpose of the formal testing of interesting economic hypotheses.                                                                                               The project consists of a methodological section and an empirical               section.  The methodological work is aimed at gaining further                   understanding of the statistical properties and the reliability                 of SNP estimators.  This work entails both Monte Carlo simulation               and derivation of analytical results.  The empirical work entails               application of the SNP methodology to issues related to labor                   market dynamics, short-term price movements on financial markets,               and nonlinear causality between money and income.  The main                     research tool for the empirical work is nonlinear maximum                       likelihood estimation.                                                                                                                                          The results of the project are expected to be useful to empirical               economists and to econometricians and statisticians interested in               time series density estimation.  Their particular appeal is that                they are not linked to a specific family of nonlinear models;                   rather the approach is nonparametric, in the sense that the                     procedures are in principle capable of capturing a wide variety                 of nonlinearities that have previously been examined only                       parametrically or only with very limited tools.
{'FirstName': 'George', 'LastName': 'Tauchen', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'George E Tauchen', 'EmailAddress': '9196843705/2723', 'NSF_ID': '000334331', 'StartDate': '08/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Duke University', 'CityName': 'DURHAM', 'ZipCode': '277054640', 'PhoneNumber': '9196843030', 'StreetAddress': '2200 W MAIN ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'North Carolina', 'StateCode': 'NC', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'NC04', 'ORG_UEI_NUM': 'TP7EK8DZV6N5', 'ORG_LGL_BUS_NAME': 'DUKE 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': '8810357.xml'}