CEIS Research Papers

Robust Tests for Convergence Clubs
Corrado Luisa, Stengos Thanasis, Weeks Melvyn and Yazgan M. Ege
CEIS Research Paper

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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Number: 451

Keywords: Multivariate stationarity, bootstrap tests, regional convergence

JEL-codes: C51,R11,R15

Volume: 17

Issue: 2

Date: 14/02/2019