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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 373 Discussion Paper ; No. 1997,27

Classification and Topics

• Classification: Wirtschaft
• Subject: Measurement Error ; Errors-in-Variables ; Functional Relations ; Non-parametric Likelihood ; Orthogonal Regression ; Partially Linear Model ; Semiparametric Models ; Structural Relations

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Liang, Hua ; Härdle, Wolfgang ; Carroll, Raymond J.
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
• (where): Berlin
• (when): 1997

Further information

• Handle: http://hdl.handle.net/10419/66252
• URN: urn:nbn:de:kobv:11-10064133
• Last update: 10.03.2025, 11:44 AM CET