Regression model

Quantile Regression

Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.

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Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI: 10.2307/1913643 ↗
Koenker, R. (2005). Quantile Regression. Cambridge University Press. DOI: 10.1017/CBO9780511754098 ↗

Referenced by

2SLS Regression ARFIMA Model Bayesian Quantile Regression Bayesian Quantile-on-Quantile Regression Bayesian Robust Regression Beta Regression Block Bootstrap Breakdown Point Analysis Conditional Value-at-Risk Conformal Prediction (Time Series)Elastic Net Regression Fourier Quantile-on-Quantile Regression GAMLSS GARCH Model Heckman Selection Model Heterogeneous Treatment Effect Fuzzy Regression Discontinuity Heterogeneous Treatment Effect Regression Discontinuity Design Heteroscedasticity-Robust Standard Errors Huber Regression Influence Diagnostics Kernel Density Estimation Least Median of Squares Least Trimmed Squares M-Estimator MAD Estimation NARDL Model Nonlinear ARDL OLS Regression Ordinal Logistic Regression Poisson Regression Probit Model Quantile-on-Quantile Regression RANSAC Regression Robust ARCH model Robust ARIMA model Robust Correlation Robust GARCH model Robust Linear Regression Robust Logistic Regression Robust Multiple linear regression Robust NARDL Robust OLS Robust Quantile Regression Robust Quantile-on-Quantile Regression Robust Regression Robust Simple linear regression Robust WLS S-Estimator Sn and Qn Scale Estimators Spatial Regression STAR Model Stochastic Frontier Analysis Structural Break Quantile-on-Quantile Regression Tail Risk Measures Theil-Sen Estimator Threshold Regression Time-varying parameter quantile-on-quantile regression Tobit Model

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