Journal of Economic Perspectives—Volume 15, Number 4—Fall 2001—Pages 143–156 Quantile Regression

Citations in the corpus

• Banks’ Noninterest Income and Systemic Risk (2020, background): “12 See Koenker and Hallock (2001) and Koenker (2005) for a detailed explanation of the quantile regression estimation methodology.”
• Conditional transformation models (2012, methodology): “This leads us to quantile regression (Koenker 2005), which is according to Stigler (2010) the “best approach to robust methods in higher dimensional linear model problems”.”
• Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions (2013, methodology): “Section 4 considers the estimation of conditional quantile functions, which constitutes a popular estimation methodology (see Koenker 2005) and may be predicated directly on an estimated conditional CDF. Section 5 assesses the finite-sample performance of the proposed method and considers an…”
• Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2013, methodology): “Condition AS(iii) is a standard assumption on the conditional density function in the quantile regression literature (see Koenker 2005) and the instrumental quantile regression literature (see Chernozhukov and Hansen 2008).”
• The Returns to Scarce Talent (2013, background): “The particular advantage of quantile regression is that it facilitates examination of salary returns to characteristics at different points in the salary distribution (Koenker, 2005; Vincent & Eastman, 2009).”
• Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution (2019, methodology): “Quantile regression has been used in a variety of applications for the estimation of the parameters in a quantile model (see Koenker 2005).”
• Do Highly Paid, Highly Skilled Women Experience the Largest Motherhood Penalty? (2016, methodology): “One of the articles that motivated our work—Budig and Hodges (2010)—used conditional quantile regression (CQR; see Hao and Naiman 2007; Koenker 2005; Koenker and Bassett 1978), but we use UQR, which was introduced into the econometric literature more recently by Firpo, Fortin, and Lemieux (2009).”
• Information Disclosure Policies: Evidence from the Electricity Industry (2007, background): “An especially common econometric application is censoring (Koenker 2005).”
• Necessary Condition Analysis (NCA) (2015, methodology): “Quantile regression (Koenker, 2005) uses all observations to draw lines.”
• Modelling species distributions using regression quantiles (2007, methodology): “…responses (e.g. expert knowledge of species environmental tolerance limits, species-environment response curves, habitat suitability indices), only one, quantile regression (or regression quantiles (RQ)), is based on well-established statistical theory (Koenker & Bassett 1978; Koenker 2005).”
• High-growth entrepreneurial firms in Africa: a quantile regression approach (2010, methodology): “By using quantile regressions, we investigate to what extent certain co-variates may affect the conditional distribution of firm growth rates more fundamentally, by changing its location, scale end shape (Koenker and Bassett 2 1978; Buchinsky 1998; Koenker 2005).”
• Conditional quantile analysis when covariates are functions, with application to growth data (2012, background): “The quantile linear regression approach in general works better than the inversion of estimated conditional distribution functions, when the assumptions for this approach are satisfied (Koenker, 2005).”
• Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison (2014, methodology): “Quantile Regression (Koenker and Bassett Jr., 1978; Koenker and Hallock, 2001; Koenker, 2005) is a regression technique335 for estimating the quantiles of a conditional distribution.”