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QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices

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  • Lung-fei Lee
  • Jihai Yu

Abstract

This paper investigates the quasi-maximum likelihood estimation of spatial dynamic panel data models where spatial weights matrices can be time varying. We find that QML estimate is consistent and asymptotically normal. We investigate marginal impacts of explanatory variables in this system via space--time multipliers. Monte Carlo results are reported to investigate the finite sample properties of QML estimates and marginal effects. When spatial weights matrices are substantially varying over time, a model misspecification of a time invariant spatial weights matrix may cause substantial bias in estimation. Slowly time varying spatial weights matrices would be of less concern. RÉSUMÉ la présente communication se penche sur l'estimation du quasi maximum de vrai semblance de modèles de données du groupe des dynamiques spatiales, où les matrices de poids spatiales peuvent varier en fonction du temps. Nous relevons que l'estimation de QML est homogène et normale sur un plan asymptotique. Nous nous penchons sur des impacts marginaux de variables causales dans ce système, par le biais de multiplicateurs spatio-temporels. Des résultats Monte Carlo sontfournis pour l'examen d’échantillons finis d'estimations QML et d'effets marginaux. Lorsque les matrices de poids spatiales varient de façon substantielle avec le temps, une erreur de spécification de modèle d'une matrice de poids spatiale ne variant pas avec le temps risquerait de fausser sensiblement les estimations. Les matrice de poids spatiale variant avec le temps auraientune importance moindre. RESUMEN Este estudio investiga la estimación casi-máxima de probabilidad de semejanza de modelos dinámicos de datos de panel en donde las matrices ponderadas espaciales pueden variar con el tiempo. Indicamos que la estimación QML es constante y asimptóticamente normal. Investigamos impactos marginales de variables explicativas en este sistema mediante multiplicadores espacio-temporales. Se informan los resultados de Monte Carlo para investigar las propiedades de muestra finitas de las estimaciones QML y los efectos marginales. Cuando las matrices ponderadas espaciales varían considerablemente en el tiempo, los errores de especificación del modelo para una matriz ponderada espacial invariable en el tiempopodrían causar una considerable parcialidad en la estimación. Las matrices de pesos espaciales variables lentos serían menos preocupantes.

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File URL: http://hdl.handle.net/10.1080/17421772.2011.647057
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Bibliographic Info

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

Volume (Year): 7 (2012)
Issue (Month): 1 (March)
Pages: 31-74

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Handle: RePEc:taf:specan:v:7:y:2012:i:1:p:31-74

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Cited by:
  1. Ho, Chun-Yu & Wang, Wei & Yu, Jihai, 2013. "Growth spillover through trade: A spatial dynamic panel data approach," Economics Letters, Elsevier, vol. 120(3), pages 450-453.
  2. Wang, Wei & Lee, Lung-fei, 2013. "Estimation of spatial panel data models with randomly missing data in the dependent variable," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 521-538.

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