Heteroskedasticity of unknown form in spatial autoregressive models with a moving average disturbance term

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsi...

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Bibliographic Details
Published in:Econometrics 2015-03, Vol.3 (1), p.101-127
Main Author: Dogan, Osman
Format: Article
Language:English
Subjects:
Asymptotic methods
asymptotics
Bias
Dependent variables
Econometrics
Generalized method of moments
heteroskedasticity
maximum likelihood estimator
Maximum likelihood method
MLE
Monte Carlo simulation
Regression analysis
SARMA(1,1)
spatial autoregressive
spatial dependence
spatial moving average
Studies
Variables
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Summary:In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of parameters of exogenous variables is inconsistent and determine its asymptotic bias. I provide simulation results to evaluate the performance of the MLE. The simulation results indicate that the MLE imposes a substantial amount of bias on both autoregressive and moving average parameters.
ISSN:2225-1146
2225-1146
DOI:10.3390/econometrics3010101