Sieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models

Liangjun Su () and Tadao Hoshino ()

No 01-2015, Working Papers from Singapore Management University, School of Economics

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 (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 C21 C23 C26 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2015-02
New Economics Papers: this item is included in nep-ecm and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in SMU Economics and Statistics Working Paper Series

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Journal Article: Sieve instrumental variable quantile regression estimation of functional coefficient models (2016)
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