Local versus Global Convergence in Europe: A Bayesian Spatial Econometric Approach
Julie Le Gallo () and
James LeSage ()
The Review of Regional Studies, 2007, vol. 37, issue 1, 82-108
Numerous studies have pointed to the econometric problems introduced by heterogeneity in cross-sectional data samples used to explore convergence suggested by neo-classical growth models. We introduce a local concept of convergence along with a Bayesian locally linear spatial estimation method to address these problems. The method allows global and local beta-convergence to be viewed in a continuous fashion. Inference regarding global convergence can be treated as a mixture distribution arising from local beta-convergence estimates from each region in the sample. Taking this approach eliminates the need to specify sub-samples and regimes as well as parameter variation schemes that have been used to model heterogeneity. We illustrate the method using a sample of 138 European regions.
Keywords: Spatial (search for similar items in EconPapers)
JEL-codes: R11 R12 R15 (search for similar items in EconPapers)
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Working Paper: Local versus Global Convergence in Europe: A bayesian Spatial Econometric Approach (2007)
Working Paper: Local versus global convergence in Europe: a bayesian spatial econometric approach (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:rre:publsh:v:37:y:2007:i:1:p:82-108
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