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Test de causalitat de Toda-Yamamoto×Prova d'arrel unitària augmentada de Dickey-Fuller (ADF)×
CampEconometriaEconometria
FamíliaRegression modelRegression model
Any d'origen19951979–1984
Autor originalToda, H. Y. and Yamamoto, T.Said & Dickey (1984); building on Dickey & Fuller (1979)
TipusCausality testHypothesis test (unit root)
Font seminalToda, 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 ↗
ÀliesToda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALDADF test, ADF unit root test, Dickey-Fuller test (augmented), Said-Dickey test
Relacionats55
ResumThe 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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ScholarGateCompara mètodes: Toda-Yamamoto causality test · Augmented Dickey-Fuller unit root test. Recuperat el 2026-06-18 de https://scholargate.app/ca/compare