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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| Format: | Article |
| Language: | English |
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MDPI AG
2015-02-01
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| Series: | Econometrics |
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| Online Access: | http://www.mdpi.com/2225-1146/3/1/101 |
| 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. |
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| ISSN: | 2225-1146 |