spreg
1.9.3.dev4+g6bcc75a56
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References
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Contents:
Installation
Tutorials
PySAL Sample Data Sets
Data Input/Output
Basic Mapping
Spatial Weights
Basic Ordinary Least Squares Regression (OLS)
Two Stage Least Squares Regression (2SLS)
Spatial Model Specifications
Spatial Multipliers
Specification Tests
Specification Tests - Properties
Distance Decay
Estimating SLX Models
Maximum Likelihood Estimation - Spatial Lag Model
Instrumental Variables Estimation - Spatial Lag Model
Maximum Likelihood Estimation - Spatial Error Model
GMM Estimation - Spatial Error Model
GMM Estimation - Higher Order Models
Spatial Panel Models in spreg
Preliminaries
Pooled panel
Fixed Effects
Random Effects
Pooled SEM - GM estimator
Pooled SEM - ML estimator
Error model - Random Effects - KKP GM Estimator
Error model - Random Effects - ML Estimator
Error Model - Fixed Effects - ML Estimator
Lag Model - Random Effects - ML Estimator
Lag Model - Fixed Effects - ML Estimator
Skater Regression
API
Classic Models
Spatial Regression Models
Discrete Choice Models
Regimes Models
Seemingly-Unrelated Regressions
Spatial Panel Models
Diagnostics
Spatial Specification Search
DGP
References
Page
spreg.dgp.make_heterror
make_heterror()
spreg.dgp.make_heterror
¶
spreg.dgp.
make_heterror
(
u
,
v
)
[source]
¶
make_heterror: transforms constant variance error term into
a heteroskedastic error vector