Compara mètodes

	Test de Cointegració Robust d'Engle-Granger	MCO robusta (MCO amb errors estàndard robustos)
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	1987 (base); robust variants 2000s–2020s	1980
Autor original	Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others	Halbert White
Tipus	Cointegration test	Linear regression with robust inference
Font seminal	Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Àlies	robust EG cointegration, outlier-robust cointegration test, robust two-step cointegration, robust EG test	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Relacionats	5	6
Resum	The Robust Engle-Granger cointegration test adapts the classic two-step Engle-Granger procedure to withstand outliers, heavy-tailed error distributions, and additive noise that can severely distort standard residual-based cointegration inference. By substituting robust regression and robust unit-root testing for classical OLS and ADF steps, it yields reliable conclusions about long-run equilibrium relationships even when the data contain anomalous observations.	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.