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Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients

Author

Listed:
  • Zongwu Cai

    (Department of Mathematics & Statistics, University of North Carolina at Charlotte
    Fujian Key Laboratory of Statistical Sciences, Xiamen University)

  • Zhijie Xiao

    (Boston College)

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.

Suggested Citation

  • Zongwu Cai & Zhijie Xiao, 2010. "Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients," Boston College Working Papers in Economics 761, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:761
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Efficiency; nonlinear time series; partially linear; partially varying coefficients; quantile regression; semiparametric;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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