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Autoren: 
Kim, Kun Ho
Chao, Shih-Kang
Härdle, Wolfgang Karl
Datum: 
2016
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2016-024
Zusammenfassung: 
In this paper, we analyze the nonparametric part of a partially linear model when the covariates in parametric and non-parametric parts are subject to measurement errors. Based on a two-stage semi-parametric estimate, we construct a uniform con dence surface of the multivariate function for simultaneous inference. The developed methodology is applied to perform inference for the U.S. gasoline demand where the income and price variables are measured with errors. The empirical results strongly suggest that the linearity of the U.S. gasoline demand is rejected.
Schlagwörter: 
Measurement error
Partially linear model
Regression calibration
Non-parametric function
Semi-parametric regression
Uniform con dence surface
Simultaneous inference
U.S. Gasoline demand
Non-linearity
JEL: 
C12
C13
C14
Dokumentart: 
Working Paper

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