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| 그랜저 인과성 검정× | 재발 정량 분석 (RQA)× | |
|---|---|---|
| 분야≠ | 계량경제학 | 복잡계 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 1969 | 2007 |
| 창시자≠ | Clive W. J. Granger | Marwan, Romano, Thiel & Kurths |
| 유형≠ | Time-series predictive causality test | Nonlinear time-series characterization |
| 원전≠ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ | Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329. DOI ↗ |
| 별칭 | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi | RQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi |
| 관련≠ | 5 | 2 |
| 요약≠ | 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. | Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems. |
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