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Methodological Approaches to Measuring Structural Convergence

Author

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  • Stela Raleva

    (University of National and World Economy, Sofia, Bulgaria)

Abstract

The paper aims to present a systematic arrangement of the most important methodological approaches to measuring the structural convergence and to study their main characteristics. Much attention is directed to the approach of identifying the catch-up process and the distributional approach which are related to the evaluation of the β and σ convergences. The most significant advantages and drawbacks of these approaches are summarized and some of the most commonly implemented in them specific methods are discussed. The time series analysis and the cluster analysis are summarized focusing on their strong and weak aspects considered from methodological and cognitive point of view.

Suggested Citation

  • Stela Raleva, 2020. "Methodological Approaches to Measuring Structural Convergence," Nauchni trudove, University of National and World Economy, Sofia, Bulgaria, issue 5, pages 157-190, December.
    • Handle: RePEc:nwe:natrud:y:2020:i:5:p:157-190
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    File URL: http://unwe-research-papers.org/bg/journalissues/article/10325
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    More about this item

    Keywords

    structural convergence; catching-up process identification approach; distributional approach; time series approach; cluster analysis approach;
    All these keywords.

    JEL classification:

    • F15 - International Economics - - Trade - - - Economic Integration
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

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