Robust Tests for Convergence Clubs

Luisa Corrado, Thanasis Stengos, Melvyn Weeks and Ege Yazgan ()

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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.

Keywords: Multivariate stationarity; bootstrap tests; regional convergence. (search for similar items in EconPapers)
JEL-codes: C51 R11 R15 (search for similar items in EconPapers)
Date: 2018-12-21
New Economics Papers: this item is included in nep-ets, nep-geo and nep-ure
Note: mw217
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Robust Tests for Convergence Clubs (2019)
Working Paper: Robust Tests for Convergence Clubs (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:1873

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