EFFICIENT SEMIPARAMETRIC ESTIMATION OF A PARTIALLY LINEAR QUANTILE REGRESSION MODEL

By S. Lee. Econometric Theory, 2003

118 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4a14e263cd9c04d1a8d7b164c37785d2a5979b8b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems (2013, unknown): “[18] derives an efficient score function for the partially linear median regression model: Si = 2φ(yi − diα0 − xiβ0)fi[di −m0(x)], where m0(xi) is m0(xi) in (B.”
Estimation of Semiparametric Models When the Criterion Function is Not Smooth (2002, unknown): “when X1i = X3i), Lee (2003) has proposed an estimation method for θo that relies on preliminary”
Massachusetts Institute of Technology Department of Economics Working Paper Series Conditional Quantile Processes Based on Series or Many Regressors Conditional Quantile Processes Based on Series or Many Regressors (unknown): “We also build on Lee [28] that studied QR estimation of partially linear models in the series framework for a single quantile index, and on Horowitz and Lee [23] that studied nonparametric QR estimation of additive quantile models for a single quantile index in a series framework.”
A Consistent Characteristic-Function-Based Test for Conditional Independence (2003, methodology): “We use Lee (2003, p. 16) to adjust h1 and h ∗ 2 appropriately in (4.3) to make sure Assumption A2 is met. It is well known that a nonparametric test that relies on the asymptotic normal approximation may perform poorly in finite samples. An alternative approach is to use bootstrap approximation. Bas”
Nonparametric Estimation of an Additive Quantile Regression Model (2004, methodology): “and Shi (1996) and Lee (2003) for quantile regression models).”
Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models (2007, methodology): “To the best of our knowledge, at this moment there is only one paper (Ai and Chen, 2003) that derived the convergence rates for the sieve MD estimates e θ n of θ o = (β o , h o ) satisfying the semiparametric conditional moment restrictions E[ρ(Z, β o , h o (·))”
Optimal regression rates for SVMs using Gaussian kernels (2013, unknown): “In [20] a partially linear quantile regression model is considered, where the parametric component learns with rate n 1 2 .”
Semiparametric quantile regression estimation in dynamic models with partially varying coefficients (2012, methodology): “Specifically, if Xt2 = 1, then, Wopt(u) = Var(Xt1|Ut = u), which is the same as that in He and Shi (1996) and Lee (2003).”
Conditional quantile processes based on series or many regressors (2011, methodology): “We also build on Lee [28] that studied QR estimation of partially linear models in the series framework for a single quantile index, and on Horowitz and Lee [23] that studied nonparametric QR estimation of additive quantile models for a single quantile index in a series framework.”
Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals (2009, methodology): “, Y2 = X2), and Lee (2003) has studied the partially linear quantile regression with exogenous Y1 and Y2 (i.”
A quantile correlated random coefficients panel data model (2016, unknown): “First, the numerator of term (42) is equal to 1 hN ∑M l=L+1 w ∗′ lNw ∗ lN p̂lN p → ∑M l=L+1 w ∗′ l w ∗ l ql|02φ0 = 2E[W∗′W∗|D = 0]φ0.”
Analyzing Semiparametrically the Trends in the Gender Pay Gap - The Example of Spain (2008, background): “Lee (2003) estimates the parametric part in an efficient way, whereas Yu (1999) proposes a two-stage estimation method.”
Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2013, background): “The main conclusion from our study is that the standard/naive post-selection inference can give misleading results, confirming our expectations and confirming predictions of Leeb and Pötscher (2005). Moreover, the proposed inference procedure is able to deliver inference of high quality, which is ve”