Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle autorégressif (AR)× | Test de causalité de Granger× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1970s (popularised 1976) | 1969 |
| Auteur d'origine≠ | George E. P. Box and Gwilym M. Jenkins | Clive W. J. Granger |
| Type≠ | Time series model | Causality test (F-test on VAR) |
| Source fondatrice≠ | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Alias | AR model, AR(p) model, autoregression, AR process | Granger test, GC test, predictive causality test, Granger non-causality test |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. | 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. |
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