방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 비선형 그레인저 인과관계 검정× | Toda-Yamamoto 인과관계 검정× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1992-2006 | 1995 |
| 창시자≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Toda, H. Y. and Yamamoto, T. |
| 유형≠ | Nonparametric causality test | Causality test |
| 원전≠ | 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. DOI ↗ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ |
| 별칭 | 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 |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|