Сравнение на методи

	Робастен анализ на панелни данни	Robust OLS (OLS с робастни стандартни грешки)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1987	1980
Създател	Arellano (1987); White (1980) heteroscedasticity-consistent framework	Halbert White
Тип	Robust estimation / inference correction	Linear regression with robust inference
Основополагащ източник	Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431–434.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Други названия	robust panel regression, cluster-robust panel estimation, panel regression with robust standard errors, HC/CR panel estimator	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Свързани	6	6
Резюме	Robust panel data analysis applies standard panel estimators — fixed effects, random effects, or pooled OLS — while replacing conventional standard errors with cluster-robust or heteroscedasticity-consistent (HC) variants. The point estimates remain unchanged; what changes is the variance-covariance matrix used for inference, making t-tests and F-tests valid even when errors are heteroscedastic or correlated within cross-sectional units over time.	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.