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.

Language: Englisch

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

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)

Event: Geistige Schöpfung

(who): Doğan, Osman

Event: Veröffentlichung

(who): MDPI

(where): Basel

(when): 2015

DOI: doi:10.3390/econometrics3010101

Handle: http://hdl.handle.net/10419/171819

Last update: 20.09.2024, 8:24 AM CEST

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Artikel

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Doğan, Osman
MDPI

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2015

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

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