Nonparametric Inference for a Triangular System of Equations for Quantile Regression

• 발행기관: 서울대학교 경제연구소
• 저자명: Yubin Kim Sungwon Lee
• 간행물 정보: 『Seoul Journal of Economics』Volume 38 No.1, 1~28쪽, 전체 28쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2025.02.28

국문 초록

In this study, we consider nonparametric estimation and inference for quantile regression (QR) with endogenous regressors. We extend the semiparametric triangular model for QR in Lee (2007) to a nonparametric one, and the identification of the structural parameters is achieved via a control function approach. Based on the identification result, we propose the use of the penalized sieve minimum distance procedure of Chen and Pouzo (2015) and develop an asymptotic theory. The inferential theory is valid regardless of whether or not the functional of the structural parameter is n-estimable, where n denotes the number of observations. We also establish the asymptotic theory for sieve quasi-likelihood ratio test statistics, enabling us to avoid estimating the asymptotic variance. A Monte Carlo simulation study shows that the proposed estimator performs well in finite samples.

목차

I. Introduction

II. Model and PSMD Estimation

III. Asymptotic Theory

IV. Monte Carlo Simulation

V. Conclusion

References

키워드

Quantile regression Endogeneity Nonparametric simultaneous equations model Sieve estimation Sieve quasi-likelihood ratio test statistics