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Spatial model selection and spatial knowledge spillovers: a regional view of Germany

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  • Klarl, Torben

Abstract

The aim of this paper is to introduce a new model selection mechanism for cross sectional spatial models. This method is more flexible than the approach proposed by Florax et al. (2003) since it controls for spatial dependence as well as for spatial heterogeneity. In particular, Bayesian and Maximum-Likelihood (ML) estimation methods are employed for model selection. Furthermore, higher order spatial influence is considered. The proposed method is then used to identify knowledge spillovers from German NUTS-2 regional data. One key result of the study is that spatial heterogeneity matters. Thus, robust estimation can be achieved by controlling for both phenomena. --

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Bibliographic Info

Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 10-005.

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Date of creation: 2010
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Handle: RePEc:zbw:zewdip:10005

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Keywords: Spatial econometrics; Bayesian spatial econometrics; Spatial heterogeneity;

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