Confidence Intervals for Regression Quantiles

By Roger W. Koenker. 1994

Subfields: Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

Autoregressive Quantile Networks for Generative Modeling (2018, background): “a central role in the analysis of quantile regression models (Koenker, 1994).”
Comparing two independent groups via the lower and upper quantiles (2014, methodology): “An alternative hypothesis testing technique is described in Wilcox [2, Section 11.1.5], again using the Koenker–Bassett estimator.”
Energy prices and CO2 emission allowance prices: A quantile regression approach (2014, methodology): “It is also efficient for problems with a large number of observations and can be used to compute the full quantile regression process (Koenker and d’Orey, 1987, 1994). Finally, the statistical inference is based on the construction of confidence intervals for the estimated parameters based on the in”
A gentle introduction to quantile regression for ecologists (2003, methodology): “Currently, the statistical theory and computational routines for estimating and making inferences on regression quantiles are best developed for the linear model (Gutenbrunner et al. 1993; Koenker 1994; Koenker and Machado 1999; Cade 2003), but are also available for parametric nonlinear (Welsh et a”
The trade-growth nexus in the developing countries: a quantile regression approach (2010, methodology): “…of the coefficient covariance matrices is an important part of quantile regression analysis and various approaches are available: bootstrap resampling methods, direct methods based on Siddiqui difference coefficients and kernel density (see Koenker and Bassett (1982), and Koenker (1994)).”
The reduced form: A simple approach to inference with weak instruments (2008, methodology): “Condition v. simply insures that the variance of α̂(β0, τ) can be consistently estimated and will be satisfied by commonly used estimators of S(β0) and Jϑ(β0); see, for example, Koenker (1994) or Fitzenberger (1998) for the dependent case.”
Quantile regression in partially linear varying coefficient models (2009, background): “Koenker [17] and Lipsitz et al. [18] discussed quantile regression for longitudinal data in linear models.”
Quantile Autoregression (2006, methodology): “A B-spline expansion QAR(1) model for Melbourne daily temperature has been described by Koenker (2000) to illustrate this approach.”
Practical Confidence Intervals for Regression Quantiles (2005, methodology): “Koenker (1994) discussed some simple ways to estimate 1/f(0) and recommended specific choices of the smoothing parameter needed there.”
A joint quantile and expected shortfall regression framework (2017, unknown): “In particular, we consider the estimators proposed by Koenker (1994), henceforth denoted by iid and by Hendricks and Koenker (1992), henceforth denoted by nid.”
Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models (2008, methodology): “Note that similar to (7), Koenker, Ng and Portnoy (1994) considered the Schwarz information criterion (SIC) of Schwarz (1978) with the second term on the right-hand side of (7) replayed by 2n ph log n, where ph is the number of “active knots” for the smoothing spline quantile setting, and Machado (1”