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Econometrics 2015, 3(1), 101-127; doi:10.3390/econometrics3010101

Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term

Program in Economics, The Graduate School and University Center, The City University of New York, New York, NY 10016, USA
Academic Editor: Giuseppe Arbia
Received: 14 October 2014 / Accepted: 27 January 2015 / Published: 26 February 2015
(This article belongs to the Special Issue Spatial Econometrics)
View Full-Text   |   Download PDF [754 KB, uploaded 26 February 2015]   |  

Abstract

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. View Full-Text
Keywords: spatial dependence; spatial moving average; spatial autoregressive; maximum likelihood estimator; MLE; asymptotics; heteroskedasticity; SARMA(1,1) spatial dependence; spatial moving average; spatial autoregressive; maximum likelihood estimator; MLE; asymptotics; heteroskedasticity; SARMA(1,1)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Doğan, O. Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term. Econometrics 2015, 3, 101-127.

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