Publication Type
Working Paper
Version
publishedVersion
Publication Date
2-2015
Abstract
In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic normality of the estimators of the functional coefficients. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis, a sequence of local alternatives and global alternatives, and propose a wild-bootstrap procedure to obtain the bootstrap p-values. A set of Monte Carlo simulations are conducted to evaluate the finite sample behavior of both the estimator and test statistic. As an empirical illustration of our theoretical results, we present the estimation of quantile Engel curves.
Keywords
Endogeneity, Functional coefficient, Heterogeneity, Instrumental variable, Panel data, Sieve estimation, Specification test, Structural quantile function
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
64
Publisher
SMU Economics and Statistics Working Paper Series, No. 01-2015
City or Country
Singapore
Citation
SU, Liangjun and HOSHINA, Tadao.
Sieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models. (2015). 1-64.
Available at: https://ink.library.smu.edu.sg/soe_research/1716
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Econometrics, https://doi.org/10.1016/j.jeconom.2015.10.006