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Spatial autoregressive models
Spatial autoregressive (SAR) models are fit using datasets that contain
observations on geographical areas or on any units with a spatial
representation. Fit linear models with autoregressive errors
and spatial lags of the dependent and independent variables.
Specify spatial lags using spatial weighting matrices.
Create standard weighting matrices, such as inverse distance or
nearest neighbor, or create custom matrices.
Fit random- and fixed-effects models for spatial panel
data. Explore direct and indirect effects of covariates after
fitting models.
Learn about Spatial autoregressive models.
SAR models for cross-sectional data
- Linear models with autoregressive errors and spatial lags of the dependent and independent variables
- Generalized method of moments estimator GS2SLS (generalized spatial two-stage least squares)
- Spatial lags and autoregressive error terms given by one or more spatial weighting matrices
- Heteroskedastic errors
- Maximum-likelihood estimator
- Robust standard errors
- Constraints
- Instrumental-variables spatial linear models
- Moran test of residual correlation
SAR models for panel data
- Fixed-effects maximum-likelihood linear models
- Random-effects maximum-likelihood linear models
- Model panel-level effects as normal i.i.d. or with the same
autoregressive form as the time-level errors
- Model panel-level effects as normal i.i.d. or with the same
autoregressive form as the time-level errors
Data management for spatial data
- Capabilities for
- Data with shapefiles
- Data without shapefiles but including location information
- Data without shapefiles or location information
- Automatic translation of standard-format shapefiles
- Set coordinates as
- Planar
- Latitude and longitude
- Calculate distances
- Automatic balancing of spatial panel data
- Draw choropleth maps
Spatial weighting matrices for SAR models
- Create and manage spatial weighting matrices that specify spatial lags
- Nearest-neighbor weighting matrices
- Inverse-distance weighting matrices
- Custom weighting matrices from
- Stata data
- Mata programs
- File import
- Stata data
- Normalization of weighting matrices
- Spectral (largest eigenvalue)
- Min–max normalization
- Row
- Manage matrices
- List
- Summarize
- Drop
- Copy
- Save and use
- Add note
- List
- Import and export weighting matrices from text files
- Use and save weighting matrices in Stata format
Predictions
- Reduced-form mean
- Direct mean
- Indirect mean
- Limited-information mean
- Full-information mean
- Linear prediction
- Residuals
- Uncorrelated residuals
Postestimation analysis
- Direct and indirect (spillover) effects with standard errors
Postestimation Selector
- View and run all postestimation features for your command
- Automatically updated as estimation commands are run
Watch Postestimation Selector.
Additional resources
See New in Stata 16 for more about what was added in Stata 16.
