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| Granger Causality× | Kointegrációs teszt (Johansen / Engle-Granger)× | Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel× | |
|---|---|---|---|
| Tudományterület | Ökonometria | Ökonometria | Ökonometria |
| Módszercsalád | Regression model | Regression model | Regression model |
| Keletkezés éve≠ | 1969 | 1988 | 2019 |
| Megalkotó≠ | Clive W. J. Granger | Engle & Granger (1987); Johansen (1988) | Wooldridge (textbook treatment); classical least squares |
| Típus≠ | Time-series predictive causality test | Time-series cointegration test | Linear regression |
| Alapmű≠ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ | Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alternatív nevek | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi | Johansen cointegration test, Engle-Granger cointegration test, long-run equilibrium test, Eşbütünleşme Testi (Johansen/Engle-Granger) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Kapcsolódó | 5 | 5 | 5 |
| Összefoglaló≠ | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. | The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988). | 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). |
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