Salīdzināt metodes

	Kvantiļu-uz-kvantiļu (QQ) regresija	ARMA modelis (Autoregresīvs vidējais aritmētiskais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2015	1970
Autors	Sim and Zhou	George E. P. Box and Gwilym M. Jenkins
Tips	Nonparametric quantile regression	Time series model
Pirmavots	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Citi nosaukumi	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
Saistītās	6	5
Kopsavilkums	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.	The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.