Best Spatial Two‐Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances

2003 ◽  
Vol 22 (4) ◽  
pp. 307-335 ◽  
Author(s):  
Lung‐fei Lee
Keyword(s):  
Least Squares ◽  
Autoregressive Model ◽  
Spatial Autoregressive Model ◽  
Two Stage ◽  
Least Squares Estimators ◽  
Spatial Autoregressive ◽  
Two Stage Least Squares

scholarly journals Lagrange Multiplier Test for Spatial Autoregressive Model with Latent Variables

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1375
Author(s):  
Anik Anekawati ◽  
Bambang Widjanarko Otok ◽  
Purhadi Purhadi ◽  
Sutikno Sutikno
Keyword(s):  
Least Squares ◽  
Lagrange Multiplier ◽  
Latent Variables ◽  
Autoregressive Model ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Equation Modeling ◽  
Spatial Autoregressive Model ◽  
Lm Test ◽  
Spatial Autoregressive

The focus of this research is to develop a Lagrange multiplier (LM) test of spatial dependence for the spatial autoregressive model (SAR) with latent variables (LVs). It was arranged by the standard SAR, where the independent variables were replaced by factor scores of the exogenous latent variables from a measurement model (in structural equation modeling) as well as their dependent variables. As a result, an error distribution of the SAR-LVs should have a different distribution from the standard SAR. Therefore, this LM test for the SAR-LVs is based on the new distribution. The estimation of the latent variables used a weighted least squares (WLS) method. The estimation of the SAR-LVs parameter used a two-stage least squares (2SLS) method. The SAR-LVs model was applied to the model with a positive and negative spatial autoregressive coefficient to illustrate how it was interpreted.

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