Regression Quantiles

By Roger W. Koenker, G. Bassett. 2007

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Robustness and generalization (2010, background): “And hence due to Breteganolle-HuberCarol inequality, the following holds with probability at least 1 − δ, K∑ i=1 ∣∣∣∣ |Ni | n − μ(Ci) ∣∣∣∣ ≤ √ 2K ln 2 + 2 ln(1/δ) n .”
Estimating Structural Changes in Regression Quantiles (2011, background): “…for research assistance and Tzuchun Kuo for suggesting the underage drunk driving data used in Section 8. yDepartment of Economics, Boston University, 270 Bay State Rd., Boston, MA, 02215 (oka@bu.edu). zDepartment of Economics, Boston University, 270 Bay State Rd., Boston, MA, 02215 (qu@bu.edu).”
The Opportunity Cost of Negative Screening in Socially Responsible Investing (2017, background): “LAD estimation, in contrast, is robust to fat tails and skewed data (Koenker and Bassett 1978; Mata and Machado 1996), as it investigates how returns are affected at the median, which is a better measure of location and estimator of central tendency. QR techniques are increasingly popular and have l”
Use of Direct Marketing Strategies by Farmers and Their Impact on Farm Business Income (2011, methodology): “Quantile regression, originally developed by Koenker and Bassett (1978), enables us to focus on the underlying socioeconomic factors influencing extreme values in the conditional distributions of the dependent variable.”
Cluster-Robust Bootstrap Inference in Quantile Regression Models (2014, methodology): “Here, I considerably extend the scope of their method to allow for inference on the entire QR process and uniformly consistent covariance matrix estimates of that process via the bootstrap; confidence intervals at individual quantiles τ j ∈ T, j = 1, 2, . . ., then follow as a special case.”
Factors affecting corporate environmental disclosure in emerging markets: The role of corporate governance structures (2020, background): “From a mathematical perspective, the quantile regression can predict the conditional median or other quantiles of the outcome variable (Koenker and Bassett, 1978), which is considered to be a more robust to outliers than least squares regression because it avoids assumptions about the parametric…”
Smoothed quantile regression for panel data (2015, unknown): “Since the seminal work of Koenker and Bassett (1978), quantile regression (QR) has attracted considerable interest in statistics and econometrics.”
A comparison of alternative methods for estimating the self-thinning boundary line (2005, unknown): “In contrast, QR solves an optimization problem of minimizing an asymmetric function of absolute error loss (Koenker and Bassett 1978; Koenker and Portnoy 1996; Bi et al. 2002).”