scholarly journals SMOOTHED EMPIRICAL LIKELIHOOD METHODS FOR QUANTILE REGRESSION MODELS

2006 ◽  
Vol 22 (02) ◽  
Author(s):  
Yoon-Jae Whang
Keyword(s):  
Quantile Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Likelihood Methods ◽  
Smoothed Empirical Likelihood

EMPIRICAL-LIKELIHOOD-BASED CONFIDENCE INTERVALS FOR CONDITIONAL VARIANCE IN HETEROSKEDASTIC REGRESSION MODELS

2010 ◽  
Vol 27 (1) ◽  
pp. 154-177 ◽  
Author(s):  
Ngai Hang Chan ◽  
Liang Peng ◽  
Dabao Zhang
Keyword(s):  
Confidence Intervals ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Variance Reduction ◽  
Normal Approximation ◽  
Conditional Variance ◽  
Likelihood Method ◽  
Simulation Studies ◽  
Likelihood Methods ◽  
Reduction Techniques

Fan and Yao (1998) proposed an efficient method to estimate the conditional variance of heteroskedastic regression models. Chen, Cheng, and Peng (2009) applied variance reduction techniques to the estimator of Fan and Yao (1998) and proposed a new estimator for conditional variance to account for the skewness of financial data. In this paper, we apply empirical likelihood methods to construct confidence intervals for the conditional variance based on the estimator of Fan and Yao (1998) and the reduced variance modification of Chen et al. (2009). Simulation studies and data analysis demonstrate the advantage of the empirical likelihood method over the normal approximation method.

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