Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Нелинеен тест за причинно-следствена връзка на Грейнджър× | Тест за причинност на Toda-Yamamoto× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1992-2006 | 1995 |
| Създател≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Toda, H. Y. and Yamamoto, T. |
| Тип≠ | Nonparametric causality test | Causality test |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | 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 |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|