Robust Tests for Convergence Clubs
44 Pages Posted: 14 Feb 2019
Date Written: December 26, 2018
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.
Keywords: multivariate stationarity, bootstrap tests, regional convergence
JEL Classification: C51, R11, R15
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