Linganisha mbinu

	Uchambuzi wa Data wa Paneli wa Ruwaza za Anga	Uchanganuzi wa Regresheni yenye Uzito wa Kijiografia wa Mizani Mingi (MGWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1988-2014	2017
Mwanzilishi	Anselin, Elhorst, and colleagues in spatial econometrics	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Aina	Spatial panel regression	Local spatial regression
Chanzo asilia	Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Majina mbadala	spatial panel model, panel spatial econometrics, spatial panel data regression, PSR	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Zinazohusiana	6	5
Muhtasari	Panel Spatial Regression extends standard panel data models by explicitly accounting for spatial dependence among cross-sectional units observed over time. It combines the temporal control of panel fixed or random effects with a spatial weights matrix that encodes geographic or network proximity, yielding unbiased and efficient estimates when observations are spatially correlated across units.	Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply.