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

Econometrics

Osman Doᇺn¹ 

[1] Program in Economics, The Graduate School and University Center, The City University of New York, New York, NY 10016, USA; E-Mail

关键词: spatial dependence;    spatial moving average;    spatial autoregressive;    maximum likelihood estimator;    MLE;    asymptotics;    heteroskedasticity;    SARMA(1;    1);   

DOI  :  10.3390/econometrics3010101

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the author; licensee MDPI, Basel, Switzerland.