Simultaneous inference for the partially linear model with a multivariate unknown function when the covariates are measured with errors

Kun Ho Kim, Shih-Kang Chao and Wolfgang Härdle

No 2016-024, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: 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.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Date: 2016
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