Heteroskedasticity of unknown form in spatial autoregressive models with a moving average disturbance term

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 1 ; Pages: 101-127 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
• Subject: spatial dependence ; spatial moving average ; spatial autoregressive ; maximum likelihood estimator ; MLE ; asymptotics ; heteroskedasticity ; SARMA(1,1)

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Doğan, Osman
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2015

Further information

• DOI: doi:10.3390/econometrics3010101
• Handle: http://hdl.handle.net/10419/171819
• Last update: 12.07.2024, 1:20 PM CEST