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| 구조적 변화 분위-분위 회귀× | 구조적 분절 그랜저 인과관계× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2015-2020s | 1995-2010 |
| 창시자≠ | Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology | Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010) |
| 유형≠ | Nonparametric quantile regression with structural breaks | Hypothesis test / time-series model |
| 원전≠ | Sim, N., and Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. 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 | SB-QQR, structural-break QQ regression, quantile-on-quantile with structural breaks, QQR with regime shifts | break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test |
| 관련≠ | 6 | 3 |
| 요약≠ | Structural Break Quantile-on-Quantile Regression (SB-QQR) extends the quantile-on-quantile framework of Sim and Zhou (2015) by allowing regression slopes to differ across regimes separated by structural breaks. It maps how the effect of a predictor's quantile on an outcome's quantile changes not only across the full distributional space but also across distinct historical periods or policy regimes. | 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. |
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