A Corrected Plug-in Method for the Quantile Confidence Interval of a Transformed Regression

• Main Authors: YANG, Zhenlin, TSE, Yiu Kuen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2002
• Subjects: Analytical calibration; Box-Cox transformation; Heteroscedasticity; Plug-in quantile limits; Regression quantile; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/697; https://ink.library.smu.edu.sg/context/soe_research/article/1696/viewcontent/Yang_Tse.pdf
• Institution: Singapore Management University

In this paper we propose an analytically corrected plug-in method for constructing confidence intervals of the conditional quantiles of a response variable with data transformation. The method can be applied to (i) a general conditional regression quantile, (ii) a general monotonic transformation, and (iii) a transformation model with heteroscedastic errors. Our results extend those in Yang (2002a), in which the median of a response variable under the Box-Cox transformation with homoscedastic errors was considered. A Monte Carlo experiment is conducted to compare the performance of the corrected plug-in method, the plug-in method and the delta method. The corrected plug-in method provides superior results over the other two methods.