Comparar métodos

	OLS Robusto (OLS com Erros Padrão Robustos)	Regressão por Mínimos Quadrados Ordinários (MQO)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1980	2019
Autor original	Halbert White	Wooldridge (textbook treatment); classical least squares
Tipo	Linear regression with robust inference	Linear regression
Fonte seminal	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Outros nomes	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	6	5
Resumo	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.	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).