Сравнение на методи

	Структурна регресия на квантили в квантил с отчитане на структурни прекъсвания	Грънджър причинно-следствена връзка със структурни прекъсвания
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2015-2020s	1995-2010
Създател	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)
Тип	Nonparametric quantile regression with structural breaks	Hypothesis test / time-series model
Основополагащ източник	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.	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.
Други названия	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
Свързани	6	3
Резюме	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.