Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230814
Authors: 
Kim, Kun Ho
Chao, Shih-Kang
Härdle, Wolfgang Karl
Year of Publication: 
2020
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2020-008
Abstract: 
In this paper, we conduct simultaneous inference of the non-parametric part of a partially linear model when the non-parametric component is a multivariate unknown function. Based on semi-parametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The developed methodology is applied to perform simultaneous inference for the U.S. gasoline demand where the income and price variables are contaminated by Berkson errors. The empirical results strongly suggest that the linearity of the U.S. gasoline demand is rejected. The results are also used to propose an alternative form for the demand.
Subjects: 
Simultaneous inference
Multivariate function
Simultaneous confidence region
Berkson error
Regression calibration
JEL: 
C12
C13
C14
Document Type: 
Working Paper

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