Gutknecht, Daniel (2012) Identification and estimation of nonlinear regression models using control functions. PhD thesis, University of Warwick.

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Abstract

According to Blundell and Powell (2003), the development of strategies to identify
and estimate certain parameters or even entire functions of regression models under
endogeneity has arguably been one of the main contributions of microeconometrics
to the statistical literature. The term endogeneity, in this context, refers to a
correlation between observable regressor(s) and model unobservable(s), which can
arise for multiple reasons such as, among others, omitted variables, measurement
error, unobserved heterogeneity, or simultaneous causality. Whereas linear identification and estimation techniques to address endogeneity date back as far as 1928
(Stock and Trebbi, 2003), advances in the field of nonlinear models are much more
recent: nonlinear parametric models under endogeneity only came under investigation
during the 1970s and 1980s (e.g. Ameniya, 1974; Hansen, 1982), and it was
not until the mid 1990s that models of (partially) unknown functional form were
considered.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Regression analysis
Official Date:	May 2012
Dates:	Date Event May 2012 Submitted
Institution:	University of Warwick
Theses Department:	Department of Economics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Corradi, Valentina ; Arulampalam, Wiji
Sponsors:	University of Warwick. Dept. of Economics ; Economic and Social Research Council (Great Britain) (ESRC) ; Royal Economic Society (Great Britain)
Extent:	vi, 110 leaves : charts
Language:	eng

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