Nonparametric Estimation of a Triangular System of Equations for Quantile Regression

• 영문명: Nonparametric Estimation of a Triangular System of Equations for Quantile Regression
• 발행기관: 한국계량경제학회
• 저자명: Sungwon Lee
• 간행물 정보: 『JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS』Vol.33 No.4, 31~53쪽, 전체 23쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 초록

We consider a class of nonparametric quantile regression (QR) models with endogenous regressors. Building upon the semiparametric QR model in Lee (2007), we develop a nonparametric framework for quantile regression in a triangular system of equations. We provide a set of conditions under which the parameters are nonparametrically identified. Then, we propose to use the penalized sieve minimum distance (PSMD) estimation approach of Chen and Pouzo (2012) to estimate the parameters. We establish the consistency and convergence rate of the PSMD estimator. Since the identification is based on a control function approach, the PSMD estimator does not suffer from an ill-posed inverse problem. A Monte-Carlo simulation study confirms that the PSMD estimator performs well in finite samples.

목차

1. INTRODUCTION

2. THE MODEL AND IDENTIFICATION

3. ESTIMATION

4. CONSISTENCY

5. CONVERGENCE RATES

6. MONTE CARLO SIMULATION

7. CONCLUSIONS

REFERENCES

키워드

Quantile Regression Endogeneity Nonparametric Simultaneous Equations Model Sieve Estimation