Artikel
Heteroskedasticity of unknown form in spatial autoregressive models with a moving average disturbance term
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 1 ; Pages: 101-127 ; Basel: MDPI
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
spatial moving average
spatial autoregressive
maximum likelihood estimator
MLE
asymptotics
heteroskedasticity
SARMA(1,1)
- DOI
-
doi:10.3390/econometrics3010101
- Handle
- Letzte Aktualisierung
-
12.07.2024, 13:20 MESZ
Objekttyp
- Artikel
Beteiligte
- Doğan, Osman
- MDPI
Entstanden
- 2015