Methoden vergelijken

	Gewone Kleinste Kwadraten (GKK) Regressie	Kwantielregressie	Robuuste OLS (OLS met Robuuste Standaardfouten)
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	2019	1978	1980
Grondlegger	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Halbert White
Type	Linear regression	Conditional quantile regression	Linear regression with robust inference
Oorspronkelijke bron	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Aliassen	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Verwant	5	5	6
Samenvatting	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.	Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.