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| Toda-Yamamoto-Kausalitätstest× | Augmented Dickey-Fuller (ADF) Einheitswurzeltest× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1995 | 1979–1984 |
| Urheber≠ | Toda, H. Y. and Yamamoto, T. | Said & Dickey (1984); building on Dickey & Fuller (1979) |
| Typ≠ | Causality test | Hypothesis test (unit root) |
| Wegweisende Quelle≠ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ | Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. DOI ↗ |
| Aliasnamen | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD | ADF test, ADF unit root test, Dickey-Fuller test (augmented), Said-Dickey test |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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 Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance. |
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