Comparar métodos

	Regressão Quantil-em-Quantil com Quebra Estrutural	Regressão Quantil-sobre-Quantil (QQ)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	2015-2020s	2015
Autor original	Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology	Sim and Zhou
Tipo	Nonparametric quantile regression with structural breaks	Nonparametric quantile regression
Fonte seminal	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.	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.
Outros nomes	SB-QQR, structural-break QQ regression, quantile-on-quantile with structural breaks, QQR with regime shifts	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression
Relacionados	6	6
Resumo	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.	Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.