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Robust Tests for Convergence Clubs

Author

Listed:
  • Corrado, L.
  • Stengos, T.
  • Weeks, M.
  • Ege Yazgan, M.

Abstract

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 finnding less convergence, is ameliorated when we utilise our bootstrap test.

Suggested Citation

  • Corrado, L. & Stengos, T. & Weeks, M. & Ege Yazgan, M., 2018. "Robust Tests for Convergence Clubs," Cambridge Working Papers in Economics 1873, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:1873
    Note: mw217
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    References listed on IDEAS

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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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