Simultaneous Inference for the Partially Linear Model with a Bi-variate Unknown Function When the Covariates are Measured with Errors
Starts: Thursday 28 January 2016
Ends: Thursday 28 January 2016
|Event type ||Seminar |
|Location ||GASK 210 |
Speaker: Kun Ho Kim (Hanyang University, South Korea)
Abstract: In this paper, we carry out inference for the bivariate unknown function in a partially linear model when covariates in parametric and non-parametric parts are subject to measurement errors. Based on its two-stage semi-parametric estimate, we construct the uniform confidence surface of the bivariate function for simultaneous inference. The proposed methodology is applied to perform inference for the U.S. gasoline demand when the income and price variables are measured with errors. The empirical results strongly suggest that there is a positive and non-linear relationship between income and gasoline demand, while the non-linearity becomes weaker for the price-gasoline demand relationship.