So sánh phương pháp

	Kiểm định nhân quả Toda-Yamamoto	Kiểm định nhân quả Granger
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1995	1969
Người khởi xướng	Toda, H. Y. and Yamamoto, T.	Clive W. J. Granger
Loại	Causality test	Causality test (F-test on VAR)
Công trình gốc	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Tên gọi khác	Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD	Granger test, GC test, predictive causality test, Granger non-causality test
Liên quan	5	5
Tóm tắt	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 Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.