Large sample theory in a semiparametric partially linear errors-in-variables models

Hua Liang, Wolfgang Härdle and Raymond J. Carroll

No 1997,27, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Abstract: We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT ß + g (T) when the X's are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis (1994) leads to biased estimates of both the parameter ß and the function g(·) when measurement error is ignored. We derive a simple modification of their estimator which is a semiparametric version of the usual parametric correction for attenuation. The resulting estimator of ß is shown to be consistent and its asymptotic distribution theory is derived. Consistent standard error estimates using sandwich-type ideas are also developed.

Keywords: Measurement Error; Errors-in-Variables; Functional Relations; Non-parametric Likelihood; Orthogonal Regression; Partially Linear Model; Semiparametric Models; Structural Relations (search for similar items in EconPapers)
Date: 1997
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:199727

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