scholarly journals Simulation Studyfor Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity

2020 ◽  
Author(s):  
Fitri Ramadhini ◽  
Anik Djuraidah ◽  
Aji Hamim Wigena
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Method ◽  
Autoregressive Models ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models

CONSISTENCY AND EFFICIENCY OF LEAST SQUARES ESTIMATION FOR MIXED REGRESSIVE, SPATIAL AUTOREGRESSIVE MODELS

2002 ◽  
Vol 18 (2) ◽  
pp. 252-277 ◽  
Author(s):  
Lung-Fei Lee
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Instrumental Variables ◽  
Estimation Method ◽  
Least Squares Estimation ◽  
Autoregressive Models ◽  
Least Squares Estimators ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Asymptotically Efficient

Least squares estimation has casually been dismissed as an inconsistent estimation method for mixed regressive, spatial autoregressive models with or without spatial correlated disturbances. Although this statement is correct for a wide class of models, we show that, in economic spatial environments where each unit can be influenced aggregately by a significant portion of units in the population, least squares estimators can be consistent. Indeed, they can even be asymptotically efficient relative to some other estimators. Their computations are easier than alternative instrumental variables and maximum likelihood approaches.

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