Spatial model selection and spatial knowledge spillovers: a regional view of Germany
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.
|Date of creation:||2010|
|Date of revision:|
|Contact details of provider:|| Postal: L 7,1; D - 68161 Mannheim|
Web page: http://www.zew.de/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:zbw:zewdip:10005. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics)
If references are entirely missing, you can add them using this form.