ScholarGate
Assistent

Methoden vergleichen

Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.

Robustes Fixed-Effects-Modell×Robuste OLS (OLS mit robusten Standardfehlern)×
FachgebietÖkonometrieÖkonometrie
FamilieRegression modelRegression model
Entstehungsjahr19871980
UrheberManuel ArellanoHalbert White
TypPanel regression with robust inferenceLinear regression with robust inference
Wegweisende QuelleArellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431–434. link ↗White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. DOI ↗
AliasnamenFE with robust standard errors, cluster-robust fixed effects, fixed effects with heteroscedasticity-robust SE, within estimator with robust inferenceHC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Verwandt56
ZusammenfassungThe robust fixed effects model combines the within-group estimator for panel data with variance-covariance matrices that remain valid under heteroscedasticity and within-unit error correlation. Introduced by Arellano (1987), cluster-robust standard errors paired with the fixed effects estimator are now the default approach for credible panel data inference in economics and social science.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.
ScholarGateDatensatz
  1. v1
  2. 2 Quellen
  3. PUBLISHED
  1. v1
  2. 2 Quellen
  3. PUBLISHED

Zur Suche Folien herunterladen

ScholarGateMethoden vergleichen: Robust Fixed Effects Model · Robust OLS. Abgerufen am 2026-06-15 von https://scholargate.app/de/compare