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


  • Klarl, Torben


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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  • Klarl, Torben, 2010. "Spatial model selection and spatial knowledge spillovers: a regional view of Germany," ZEW Discussion Papers 10-005, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  • Handle: RePEc:zbw:zewdip:10005

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    More about this item


    Spatial econometrics; Bayesian spatial econometrics; Spatial heterogeneity;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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