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

Klassifikation
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
Thema
spatial dependence
spatial moving average
spatial autoregressive
maximum likelihood estimator
MLE
asymptotics
heteroskedasticity
SARMA(1,1)

Ereignis
Geistige Schöpfung
(wer)
Doğan, Osman
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2015

DOI
doi:10.3390/econometrics3010101
Handle
Letzte Aktualisierung
12.07.2024, 13:20 MESZ

Objekttyp

  • Artikel

Beteiligte

  • Doğan, Osman
  • MDPI

Entstanden

  • 2015

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