Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Панельная квантиль-на-квантиль регрессия× | Тест причинности по Грейнджеру на панельных данных× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2015 (QQ); panel applications from ~2018 | 1988–2012 |
| Автор метода≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012) |
| Тип≠ | Nonparametric quantile regression | Causality test |
| Основополагающий источник≠ | 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 ↗ | Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. DOI ↗ |
| Другие названия | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | panel causality test, Dumitrescu-Hurlin test, heterogeneous panel causality, panel Granger test |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Panel quantile-on-quantile (QQ) regression jointly maps any quantile of the outcome distribution onto any quantile of the predictor distribution across multiple cross-sectional units observed over time. It generalises Sim and Zhou's (2015) cross-sectional QQ framework to a panel setting, revealing a full dependence surface rather than a single average effect, while accounting for individual heterogeneity through fixed or random effects correction. | The Panel Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by pooling information across units. |
| ScholarGateНабор данных ↗ |
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