Sieve instrumental variable quantile regression estimation of functional coefficient models

Liangjun Su, Tadao Hoshino

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    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 estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients and study its asymptotic. We conduct simulations to evaluate the finite sample behavior of our estimator and test statistic, and apply our method to study the estimation of quantile Engel curves.

    Original languageEnglish
    Pages (from-to)231-254
    Number of pages24
    JournalJournal of Econometrics
    Volume191
    Issue number1
    DOIs
    Publication statusPublished - 2016 Mar 1

    Fingerprint

    Quantile Estimation
    Regression Estimation
    Quantile Regression
    Instrumental Variables
    Sieves
    Sieve
    Coefficient
    Uniform Consistency
    Estimator
    Specification Test
    Uniform Asymptotics
    Non-parametric test
    Quantile
    Asymptotic Normality
    Statistics
    Model
    Estimate
    Test Statistic
    Specifications
    Covariates

    Keywords

    • C13
    • C14
    • C21
    • C23
    • C26
    • JEL classification C12

    ASJC Scopus subject areas

    • Economics and Econometrics
    • Applied Mathematics
    • History and Philosophy of Science

    Cite this

    Sieve instrumental variable quantile regression estimation of functional coefficient models. / Su, Liangjun; Hoshino, Tadao.

    In: Journal of Econometrics, Vol. 191, No. 1, 01.03.2016, p. 231-254.

    Research output: Contribution to journalArticle

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