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 1:00 pm
Ends: Thursday 28 January 2016 2:00 pm
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.

Page last updated: Wednesday 27 January 2016