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| Test de Causalité de Granger de Toda-Yamamoto× | Autoregressive Vectoriel (VAR)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille≠ | Hypothesis test | Regression model |
| Année d'origine≠ | 1995 | 1980 |
| Auteur d'origine≠ | Hiro Toda & Taku Yamamoto | Christopher A. Sims |
| Type≠ | Modified Wald test on augmented VAR | 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 ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Alias | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Apparentées≠ | 3 | 5 |
| Résumé≠ | The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
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