手法を比較

	非線形グレンジャー因果性検定	戸田・山本の因果性検定
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	1992-2006	1995
提唱者	Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)	Toda, H. Y. and Yamamoto, T.
種類	Nonparametric causality test	Causality test
原典	Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.
別名	nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality	Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD
関連	6	5
概要	Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture.	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.