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Convergence clustering with forecast densities

Author

Listed:
  • Ingianni, Andrea

    (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.

Suggested Citation

  • Ingianni, Andrea, 2017. "Convergence clustering with forecast densities," Economics Discussion Papers 2017-6, School of Economics, Kingston University London.
    • Handle: RePEc:ris:kngedp:2017_006
    as

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    More about this item

    Keywords

    Convergence hypothesis; Growth models; Cluster analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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