Accomplishment-Based Renewal:  Investigations in EconometricModels                                                                               >
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{'SignBlockName': 'Robin A. Cantor', 'PO_EMAI': None, 'PO_PHON': None}
This project is an important extension of the recent work of this very          talented researcher.  The general area in which Professor Hausman is            working in known as the errors-in-variables econometric model.  It is           well known that variables used in economic research are susceptible to          measurement error.  The errors can derive from many sources.  For               example, published survey data might include clerical errors or                 incorrect information, and accounts of national expenditures,                   investment, income and so on are highly aggregated.  The magnitude of           the errors is not known to the researcher, but the distributional form          the errors might take can be assumed.  Since the effects that these             errors can have on the final analytical results depend strongly on the          distributional form and on the statistical estimation technique,                knowing precisely the properties of various error generating processes          is therefore quite important.                                                                                                                                   Past research by Professor Hausman and his colleagues has shown that            errors of measurement are not so severe if the errors are linear.  In           this case many of the usual econometric estimation techniques yield             consistent estimates.  If, however, the errors follow a nonlinear               process, the effects on the results are much more pronounced.                                                                                                   In this project Professor Hausman analyzes the effects of nonlinear             errors in econometric models.  He shows that commonly used techniques           produce inconsistent estimates, and thus ambiguous results.  New                estimators are derived which yield consistent estimates in a general            nonlinear model, and the asymptotic properties of these new estimators          are examined.  By approximating the nonlinear error process with a              general, semiparametric linear process, Professor Hausman derives               instrumental variables which allow for the identification and                   consistent estimation of structural econometric models.  The                    techniques are applied to data from the Consumer Expenditure Survey             for estimate expenditure functions for food in the U.S.
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{'Code': '1320', 'Text': 'Economics'}
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