Spatial model selection and spatial knowledge spillovers: a regional view of Germany
AbstractThe 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 InfoPaper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 10-005.
Date of creation: 2010
Date of revision:
Spatial econometrics; Bayesian spatial econometrics; Spatial heterogeneity;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-06 (All new papers)
- NEP-CSE-2010-03-06 (Economics of Strategic Management)
- NEP-ECM-2010-03-06 (Econometrics)
- NEP-GEO-2010-03-06 (Economic Geography)
- NEP-URE-2010-03-06 (Urban & Real Estate Economics)
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