Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients
AbstractWe 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.
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Bibliographic InfoPaper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 761.
Date of creation: 22 Nov 2010
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Efficiency; nonlinear time series; partially linear; partially varying coefficients; quantile regression; semiparametric;
Other versions of this item:
- Cai, Zongwu & Xiao, Zhijie, 2012. "Semiparametric quantile regression estimation in dynamic models with partially varying coefficients," Journal of Econometrics, Elsevier, vol. 167(2), pages 413-425.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-27 (All new papers)
- NEP-ECM-2010-11-27 (Econometrics)
- NEP-ETS-2010-11-27 (Econometric Time Series)
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