Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskais laika telpiskais nobīdes modelis× | Telpiskā autokorelācija× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2003-2008 | 1950 |
| Autors≠ | Anselin, Le Gallo & Jayet; Elhorst | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Tips≠ | Spatial panel regression | Spatial statistic / exploratory spatial data analysis |
| Pirmavots≠ | Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial Panel Econometrics. In L. Matyas & P. Sevestre (Eds.), The Econometrics of Panel Data (pp. 625-660). Springer. link ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Citi nosaukumi | ST-SAR, spatial-temporal lag model, spatiotemporal autoregressive model, space-time SAR model | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Space-Time Spatial Lag Model extends the classic spatial autoregressive (SAR) lag model to panel data, capturing how the outcome in each location at each time point is influenced by the contemporaneous outcomes of neighboring locations, while also controlling for unit-specific and time-specific fixed effects. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
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