Simultaneous Inference for the Partially Linear Model with A Multivariate Unknown Function When the Covariates are Measured with Errors

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 confidence 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

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Year of publication:	2016
Authors:	Kim, Kun Ho
Other Persons:	Chao, Shih-Kang (contributor) ; Härdle, Wolfgang (contributor)
Publisher:	[2016]: [S.l.] : SSRN
Subject:	Schätztheorie \| Estimation theory \| Statistische Methodenlehre \| Statistical theory \| Induktive Statistik \| Statistical inference

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