Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term
In this study, I investigate the necessary condition for 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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| Length: | 33 |
| Date of creation: | 16 Dec 2013 |
| Date of revision: | |
| Handle: | RePEc:cgc:wpaper:002 |
| Contact details of provider: | Postal: 365 5th Avenue, 5th Floor, New York, NY 10026 Phone: (212) 817-8255 Fax: (212) 817-1514 Web page: http://www.gc.cuny.edu/economics More information through EDIRC
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- Osman Dogan & Suleyman Taspinar, 2013. "GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," Working Papers 1, City University of New York Graduate Center, Ph.D. Program in Economics.
- Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
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