방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Hatemi-J 비대칭 인과관계 검정× | Toda-Yamamoto (TY) 인과관계 검정× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 2012 | 1995 |
| 창시자≠ | Abdulnasser Hatemi-J | Hiro Toda & Taku Yamamoto |
| 유형≠ | Nonlinear Granger causality test | Modified Wald test on augmented VAR |
| 원전≠ | Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447–456. 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 | Hatemi-J Asymmetric Causality Test, Asymmetric Causality Test, Positive and Negative Causality Test, Asimetrik Nedensellik Testi | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi |
| 관련 | 3 | 3 |
| 요약≠ | The Hatemi-J asymmetric causality test, introduced by Abdulnasser Hatemi-J in 2012, extends the Granger causality framework to allow causal relationships between the positive and negative components of integrated time series to differ. By decomposing each series into cumulative positive and negative partial sums and embedding the Toda-Yamamoto approach within a VAR, the test enables researchers to distinguish whether positive shocks, negative shocks, or both drive causation between economic variables. | The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic. |
| ScholarGate데이터셋 ↗ |
|
|