Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Test de cointegración de Engle-Granger× | Prueba de Causalidad de Granger× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1987 | 1969 |
| Autor original≠ | Robert F. Engle and Clive W. J. Granger | Clive W. J. Granger |
| Tipo≠ | Cointegration test | Causality test (F-test on VAR) |
| Fuente seminal≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Alias | EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test | Granger test, GC test, predictive causality test, Granger non-causality test |
| Relacionados | 5 | 5 |
| Resumen≠ | The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment. | The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. |
| ScholarGateConjunto de datos ↗ |
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