Conditional Quantile Methods in Econometrics
{'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 support is for the continuation of a very interesting and                  important line of research in theoretical econometrics.  Most applied           econometric research involves using a statistical estimation technique          known as "least squares" to analyze data.  In essence this technique            acknowledges that there are errors in the data, and these errors can            derive from many sources.  For instance, reported measurements of               prices or incomes might be rounded to the nearest dollar rather than            being exact.  National economic data are highly aggregated, and survey          data might include various types of clerical errors.  An analyst, of            course, knows that errors will exist in the data, but does not know             their  magnitudes.  However, in order to draw consistent and                    unambiguous conclusions, any analysis of such data must incorporate             the capability to account for the potential effects of the errors               inherent in the data.                                                                                                                                           Fortunately, the size of the errors can be estimated statistically,             and the distribution of the errors can be assumed.  Typically errors            are assumed to follow the normal distribution.  A proven analytical             technique computes results by choosing parameters such that the sum of          the squares of the estimated error values is at a minimum, hence the            name "least squares".  This technique forms the basis for virtually             all economic analysis, thus knowing its inherent drawbacks is very              important.  Much research has shown the results of least squares                estimation not to be robust if the true distribution of the errors is           not normal.  This project, and the line of work to which it belongs,            presents an alternative to least squares estimation that is not so              dependent on the errors being normally distributed, and so represents           an important contribution to econometric analysis.                                                                                                              Technically, this project develops a method of statistical estimation           and inference based on regression quantiles.  These are linear                  combinations of statistics based on data which have been ordered in             some way.  By studying these combinations, Professor Koenker derives            estimation techniques robust to distributional errors.  He also                 extends his analysis to estimation of summary statistics and                    confidence intervals.
{'FirstName': 'Roger', 'LastName': 'Koenker', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Roger Koenker', 'EmailAddress': 'Rkoenker@uiuc.edu', 'PI_PHON': '2173334558', 'NSF_ID': '000401394', 'StartDate': '07/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1320', 'Text': 'Economics'}
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