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| Regresi Kuantil-pada-Kuantil Panel× | Panel OLS (Pooled Ordinary Least Squares)× | |
|---|---|---|
| Bidang | Ekonometrika | Ekonometrika |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2015 (QQ); panel applications from ~2018 | 1986-2003 |
| Pencetus≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Classical least squares applied to pooled panels; foundational treatment in Hsiao (2003) and Wooldridge (2010) |
| Tipe≠ | Nonparametric quantile regression | Linear panel regression |
| Sumber perintis≠ | 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 ↗ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Alias | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | pooled OLS, pooled ordinary least squares, panel least squares, POLS |
| Terkait≠ | 6 | 4 |
| Ringkasan≠ | 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. | Panel OLS — also called Pooled OLS — applies the classical ordinary least squares estimator to panel data by stacking all cross-sectional units and time periods into a single sample. It estimates one common set of slope coefficients under the assumption that the intercept and slopes are homogeneous across units and time. |
| ScholarGateSet data ↗ |
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