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| 구조적 분절 그랜저 인과관계× | 그랜저 인과성 검정× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1995-2010 | 1969 |
| 창시자≠ | Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010) | Clive W. J. Granger |
| 유형≠ | Hypothesis test / time-series model | Time-series predictive causality test |
| 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 ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ |
| 별칭 | break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi |
| 관련≠ | 3 | 5 |
| 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 Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. |
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