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Robust Tests for Convergence Clubs

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In many applications common in testing for convergence the number of cross-sectional units is large and the number of time periods are few. In these situations asymptotic tests based on an omnibus null hypothesis are characterised by a number of problems. In this paper we propose a multiple pairwise comparisons method based on an a recursive bootstrap to test for convergence with no prior information on the composition of convergence clubs. Monte Carlo simulations suggest that our bootstrap-based test performs well to correctly identify convergence clubs when compared with other similar tests that rely on asymptotic arguments. Across a potentially large number of regions, using both cross-country and regional data for the European Union we find that the size distortion which afflicts standard tests and results in a bias towards finding less convergence, is ameliorated when we utilise our bootstrap test.

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  • Luisa Corrado & Thanasis Stengos & Melvyn Weeks & M. Ege Yazgan, 2019. "Robust Tests for Convergence Clubs," CEIS Research Paper 451, Tor Vergata University, CEIS, revised 14 Feb 2019.
    • Handle: RePEc:rtv:ceisrp:451
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    1. Nghiem, Son & Tran, Bach & Afoakwah, Clifford & Byrnes, Joshua & Scuffham, Paul, 2021. "Wealthy, healthy and green: Are we there yet?," World Development, Elsevier, vol. 147(C).

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    More about this item

    Keywords

    Multivariate stationarity; bootstrap tests; regional convergence.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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