방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 구조적 분절 그랜저 인과관계× | Toda-Yamamoto (TY) 인과관계 검정× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열≠ | Regression model | Hypothesis test |
| 기원 연도≠ | 1995-2010 | 1995 |
| 창시자≠ | Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010) | Hiro Toda & Taku Yamamoto |
| 유형≠ | Hypothesis test / time-series model | Modified Wald test on augmented VAR |
| 원전≠ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. 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 | break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi |
| 관련 | 3 | 3 |
| 요약≠ | Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables switches on, switches off, or changes direction over time. | 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 ↗ |
|
|