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| Regresja kwantylowa na kwantylach (QQ)× | Model ARMA (Autoregresyjny Model Średniej Ruchomej)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2015 | 1970 |
| Twórca≠ | Sim and Zhou | George E. P. Box and Gwilym M. Jenkins |
| Typ≠ | Nonparametric quantile regression | Time series model |
| Źródło pierwotne≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Inne nazwy | QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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