Convergence clustering with forecast densities

Andrea Ingianni

No 2017-6, Economics Discussion Papers from School of Economics, Kingston University London

Abstract: The paper proposes an approach to output convergence based on time-series clustering algorithms. Traditional clustering methods tend to ignore the autocorrelation structure of time-series and make computations in the time-domain difficult. We show how by focusing on forecast densities it is possible to bring this important dimension in empirical tests of the convergence process. The approach is illustrated with a standard application to the case of New Zeland and her four major trading partners after the 1950s. Results offer insights in the findings of the existing empirical literature.

Keywords: Convergence hypothesis; Growth models; Cluster analysis (search for similar items in EconPapers)
JEL-codes: C32 C38 O40 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2017-12-10
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Persistent link: https://EconPapers.repec.org/RePEc:ris:kngedp:2017_006

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