Conditional empirical likelihood estimation and inference for quantile regression models

By Taisuke Otsu. 2008

59 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/759677507f49bf2ede5a784aa02092081f74c2eb

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In Economics and Econometrics

1. Yunwen Yang
2. Xuming He
3. David M. Kaplan
4. Huixia Judy Wang
5. Huixia Judy Wang
6. Zhongyi Zhu
7. Yixiao Sun
8. Timothy J. Vogelsang

In Finance

1. David M. Kaplan
2. Huixia Judy Wang
3. Yunwen Yang
4. Huixia Judy Wang
5. Zhongyi Zhu
6. Yixiao Sun
7. Xuming He
8. Morten Ørregaard Nielsen

In Statistics and Probability

1. Xuming He
2. Yunwen Yang
3. Zhongyi Zhu
4. Huixia Judy Wang
5. Huixia Judy Wang
6. David M. Kaplan
7. Yixiao Sun
8. Ying Wei

Citations in the corpus

• Empirical likelihood for quantile regression models with longitudinal data (2011, methodology): “Using similar arguments as above, we obtain lðb0Þ ¼ n n 1 Xn i ¼ 1 Ziðb0Þ ( )T n 1 Xn i ¼ 1 Ziðb0ÞZiðb0ÞT ( ) 1 n 1 Xn i ¼ 1 Ziðb0Þ ( ) þopð1Þ: ð13Þ By the central limit theorem, we have n1=2Dn-Nð0,SÞ: ð14Þ Similar to the proof of (28)–(30) in Otsu (2008), we have b̂EL b0 ¼Opðn 1=2Þ.”
• Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, methodology): “…X D x/ D x>ˇ. /: (3.2) Various estimation methods have been developed for censored quantile regression, including Portnoy (2003), Ying et al. (1995), Peng & Huang (2008) and Wang & Wang (2009) for random censoring, Lin et al. (2012) for double censoring and Powell (1986) and Tang et al. (2012) for…”
• Bayesian empirical likelihood for quantile regression (2012, methodology): “Following Koenker and Bassett ( 1978), we specify the τ th conditional quantile function of Y ∈ R given X ∈ R p +1 as including Chen and Hall (1993), Whang (2006) and Otsu (2008) proposed using smoothed versions of the quantile estimating equations.”
• SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION (2016, methodology): “For QR estimation and inference via empirical likelihood, Otsu (2008) and Whang (2006) also examined smoothed estimators.”
• Smoothing Quantile Regressions (2019, unknown): “…including bootstrapping (Horowitz 1998; Machado and Parente 2005), MCMC methods (Chernozhukov and Hong 2003), empirical likelihood (Whang 2006; Otsu 2008), strong approximation methods (Portnoy 2012), nonstandard inference (Goh and Knight 2009), and other nonparametric approaches (Fan and Liu…”