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Maximum Likelihood Estimation of a General Unbalanced Spatial Random Effects Model: a Monte Carlo Study

Listed author(s):
  • Michael Pfaffermayr

Abstract This paper discusses the maximum likelihood estimator of a general unbalanced spatial random effects model with normal disturbances, assuming that some observations are missing at random. Monte Carlo simulations show that the maximum likelihood estimator for unbalanced panels performs well and that missing observations affect mainly the root mean square error. As expected, these estimates are less efficient than those based on the unobserved balanced model, especially if the share of missing observations is large or spatial autocorrelation in the error terms is pronounced. Estimation de vraisemblance maximale d'un modèle général d'effets aléatoires spatiaux déséquilibré: une étude Monte Carlo RÉSUMÉ La présente communication se penche sur l'estimateur du maximum de vraisemblance d'un modèle général d'effets aléatoires spatiaux déséquilibré avec des perturbations normales, en supposant l'absence aléatoire de certaines observations. Des simulations de Monte Carlo montrent que des groupes déséquilibrés se comporte bien, et que les observations manquantes affectent principalement l'erreur de la moyenne quadratique. Comme prévu, ces évaluations sont moins efficaces que celles qui sont basées sur le modèle équilibré non observé, notamment si la part des observations manquantes est importantes, ou l'on déclare une autocorrélation spatiale dans les termes d'erreur. Estimación de la probabilidad máxima de un modelo espacial general desequilibrado de efectos al azar: un estudio de Monte Carlo RÉSUMÉN Este trabajo discute el estimador de probabilidad máxima de un modelo espacial general desequilibrado de efectos al azar con alteraciones normales, suponiendo que faltan algunas observaciones al azar. Las simulaciones de Monte Carlo muestran que el estimador de probabilidad máxima para los paneles desequilibrados funciona satisfactoriamente, y que las observaciones omisas afectan principalmente al error de la media cuadrática. Como se suponía, estas estimaciones son menos eficientes que las basadas en el modelo equilibrado inadvertido, especialmente si la cantidad de omisiones es grande/o la autocorrelación en los términos de error es pronunciada.

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Article provided by Taylor & Francis Journals in its journal Spatial Economic Analysis.

Volume (Year): 4 (2009)
Issue (Month): 4 ()
Pages: 467-483

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Handle: RePEc:taf:specan:v:4:y:2009:i:4:p:467-483
DOI: 10.1080/17421770903317645
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