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Spatial Distribution Dynamics

Author

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  • Stefano Magrini

    ()

  • Margherita Gerolimetto

Abstract

It is quite common in convergence analyses across regions that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools for addressing the issue, research is only at a very initial stage within the distribution dynamics approach. In particular, in the continuous state-space framework, a few authors opted for spatial pre-filtering the data in order to guarantee the statistical properties of the estimates. In this paper we follow an alternative route that starts from the idea that spatial dependence is not just noise but can be a substantive element of the data generating process. In particular, we develop a tool that, building on the mean-bias adjustment procedure proposed by Hyndman et al. (1996), explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for pre-filtering. Using this tool, we then reconsider the evidence on convergence across regional economies in the US.

Suggested Citation

  • Stefano Magrini & Margherita Gerolimetto, 2015. "Spatial Distribution Dynamics," ERSA conference papers ersa15p1172, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa15p1172
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    File URL: http://www-sre.wu.ac.at/ersa/ersaconfs/ersa15/e150825aFinal01172.pdf
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    References listed on IDEAS

    as
    1. Florax, Raymond & Folmer, Henk, 1992. "Specification and estimation of spatial linear regression models : Monte Carlo evaluation of pre-test estimators," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 405-432, September.
    2. Naisyin Wang, 2003. "Marginal nonparametric kernel regression accounting for within-subject correlation," Biometrika, Biometrika Trust, vol. 90(1), pages 43-52, March.
    3. Roberto Basile, 2010. "Intra-distribution dynamics of regional per-capita income in Europe: evidence from alternative conditional density estimators," Statistica, Department of Statistics, University of Bologna, vol. 70(1), pages 3-22.
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    Cited by:

    1. repec:grm:ecoyun:201717 is not listed on IDEAS
    2. Davide FIASCHI & Lisa GIANMOENA & Angela PARENTI, 2014. "Local Directional Moran Scatter Plot - Ldms," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 97-112.
    3. Carlos Mendez-Guerra, 2017. "Heterogeneous Growth and Regional (Di)Convergence in Bolivia: A Distribution Dynamics Approach," Economía Coyuntural,Revista de temas de perspectivas y coyuntura, Instituto de Investigaciones Económicas y Sociales 'José Ortiz Mercado' (IIES-JOM), Facultad de Ciencias Económicas, Administrativas y Financieras, Universidad Autónoma Gabriel René Moreno, vol. 2(4), pages 81-108.

    More about this item

    Keywords

    immigration; convergence; distribution dynamics; spatial effects;

    JEL classification:

    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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