Сравнение на методи

	Robustен тест за коинтеграция на Енгъл-Грейнджър	Robust OLS (OLS с робастни стандартни грешки)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1987 (base); robust variants 2000s–2020s	1980
Създател	Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others	Halbert White
Тип	Cointegration test	Linear regression with robust inference
Основополагащ източник	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.
Други названия	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
Свързани	5	6
Резюме	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.