Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients

Zongwu Cai () and Zhijie Xiao

No 761, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: We study quantile regression estimation for dynamic models with partially varying coefficients so that the values of some coefficients may be functions of informative covariates. Estimation of both parametric and nonparametric functional coefficients are proposed. In particular, we propose a three stage semiparametric procedure. Both consistency and asymptotic normality of the proposed estimators are derived. We demonstrate that the parametric estimators are root-n consistent and the estimation of the functional coefficients is oracle. In addition, efficiency of parameter estimation is discussed and a simple efficient estimator is proposed. A simple and easily implemented test for the hypothesis of varying-coefficient is proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed estimators.

Keywords: Efficiency; nonlinear time series; partially linear; partially varying coefficients; quantile regression; semiparametric (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2010-11-22
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Semiparametric quantile regression estimation in dynamic models with partially varying coefficients (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:761

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