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| Test de Causalité de Toda-Yamamoto× | Modèle à Correction d'Erreur Vectorielle (VECM)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1995 | 1987 |
| Auteur d'origine≠ | Toda, H. Y. and Yamamoto, T. | Robert F. Engle and Clive W. J. Granger |
| Type≠ | Causality test | Multivariate time-series model |
| Source fondatrice≠ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| Alias | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model |
| Apparentées | 5 | 5 |
| Résumé≠ | The Toda-Yamamoto (TY) causality test is a modified Wald procedure for testing Granger causality in vector autoregressions (VARs) estimated in levels, even when variables are nonstationary or cointegrated. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, it restores the standard chi-squared asymptotic distribution of the Wald statistic without requiring prior unit-root or cointegration pretesting. | The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series. |
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