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Toda-Yamamotovo test kauzalnosti×Vektorski model korekcije pogreške (VECM)×
PodručjeEkonometrijaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka19951987
TvoracToda, H. Y. and Yamamoto, T.Robert F. Engle and Clive W. J. Granger
VrstaCausality testMultivariate time-series model
Temeljni izvorToda, 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 ↗
Drugi naziviToda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALDVECM, error correction VAR, cointegrated VAR, vector equilibrium correction model
Srodne55
SažetakThe 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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ScholarGateUsporedite metode: Toda-Yamamoto causality test · Vector Error Correction Model. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare