Comparar métodos

	Regresión Cuantil-sobre-Cuantil en Panel	Mínimos Cuadrados Generalizados para Datos de Panel (Panel GLS)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	2015 (QQ); panel applications from ~2018	1935 / developed for panels 1980s–1990s
Autor original	Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics	Aitken (1935); extended to panel data by Baltagi and others
Tipo	Nonparametric quantile regression	Generalized linear regression
Fuente seminal	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.	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	Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel
Relacionados	6	3
Resumen	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 GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified.