Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Universal Kriging× | Telpiskās regresijas paneļa analīze× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1963 (base method); panel extension: 1990s–2000s | 1988-2014 |
| Autors≠ | Matheron, G.; extended to panel settings by geostatistical literature | Anselin, Elhorst, and colleagues in spatial econometrics |
| Tips≠ | Geostatistical interpolation | Spatial panel regression |
| Pirmavots≠ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 |
| Citi nosaukumi | UK panel interpolation, panel UK, universal kriging for panel data, longitudinal universal kriging | spatial panel model, panel spatial econometrics, spatial panel data regression, PSR |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Panel Universal Kriging extends Universal Kriging to data structures with repeated spatial observations over time (panel or longitudinal format). It simultaneously estimates a deterministic trend surface — incorporating covariates that vary across both space and time — and a stochastic spatially correlated residual, pooling information across all time periods to improve prediction accuracy and parameter stability. | 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. |
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