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Local versus Global Convergence in Europe: A Bayesian Spatial Econometric Approach

Cem Ertur, Julie Le Gallo () and James LeSage ()

The Review of Regional Studies, 2007, vol. 37, issue 1, 82-108

Abstract: 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)
Date: 2007
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http://journal.srsa.org/ojs/index.php/RRS/article/view/138/89 (application/pdf)

Related works:
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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