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

  • Ertur, Cem

    (U Orleans)

  • Le Gallo, Julie

    (CRESE, U Franche-Comte)

  • LeSage, James P.

    (TX State U)

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.

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Article provided by Southern Regional Science Association in its journal Review of Regional Studies.

Volume (Year): 37 (2007)
Issue (Month): 1 ()
Pages: 82-108

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Handle: RePEc:rre:publsh:v:37:y:2007:i:1:p:82-108
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