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(VF)Économétrie spatiale et données spatiales empilées dans le temps : Proposition d'une modélisation adaptée.(VA)Spatial Econometric and Spatial Data Pooled over Time: Towards an adapted modelling approach

Author

Listed:
  • Jean Dubé

    (Université du Québec à Rimouski)

  • Diègo Legros

    (LEG/AMIE - CNRS FRE 3496 - Université de Bourgogne)

Abstract

(VF)L'article s'attache à présenter la particularité des données spatiales empilées et montre pourquoi il peut être contre-indiqué d'utiliser les approches spatiales développées pour les données en coupe transversale empilées dans le temps. La dimension temps implique des relations unidirectionnelles, par opposition aux relations multidirectionnelles spatiales. La construction d'une matrice de pondérations spatio-temporelles unique, à partir de matrices de pondérations spatiales et temporelles, permet d'utiliser les modèles et les tests développés pour les données spatiales tout en tenant compte des deux dimensions simultanément. Une série d'applications empiriques montre que la non prise en compte de la dimension temporelle dans les analyses a pour conséquence de surévaluer les mesures de la dépendance spatiale en plus de surévaluer les coefficients autorégressifs spatiaux estimés. Finalement, la prise en compte des deux dimensions, spatiales et temporelles, permet de générer de nouvelles variables explicatives dynamiques, comparables à des effets de pairs, qui s'avèrent significatives dans l’explication des prix de vente immobiliers. (VA)This paper presents the characteristics of spatial data pooled over time and show why these data bases cannot be considered as the same way as spatial panel data or strictly spatial data. The temporal dimension implies a unidirectionality of relations, while spatial relations are multidirectional. The construction of spatio-temporal weights matrix, lying on spatial and temporal weights matrices, allow to use usual statistic models and tests developed for spatial analysis while accounting simultaneously for temporal and spatial dimensions. Empirical examples established the impact of neglecting the temporal dimension in spatial analysis and show how such approach overestimate the pattern of spatial dependence as well as overestimate the spatial autoregressive coefficient estimated. Finally, accounting for both dimensions, spatial and temporal, allow to generate additional independent variables, considering dynamic effect, that appear to play a significant role on determination of real estate prices.

Suggested Citation

  • Jean Dubé & Diègo Legros, 2013. "(VF)Économétrie spatiale et données spatiales empilées dans le temps : Proposition d'une modélisation adaptée.(VA)Spatial Econometric and Spatial Data Pooled over Time: Towards an adapted modelling ap," LEG - Document de travail - Economie 2013-01, LEG, Laboratoire d'Economie et de Gestion, CNRS, Université de Bourgogne.
  • Handle: RePEc:lat:legeco:e2013-01
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