Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term
No 2, Working Papers from City University of New York Graduate Center, Ph.D. Program in Economics
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.
Keywords: spatial dependence; spatial moving average; spatial autoregressive; maximum likelihood estimator; MLE; asymptotics; heteroskedasticity; SARMA(1; 1) (search for similar items in EconPapers)
JEL-codes: C13 C21 C31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-geo, nep-ore and nep-ure
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http://wfs.gc.cuny.edu/Economics/RePEc/cgc/wpaper/CUNYGC-WP002R.pdf Revised version, December 2014 (application/pdf)
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