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Testing the convergence and the divergence in five Asian countries: from a GMM model to a new Machine Learning algorithm

Authors: Cosimo Magazzino; Marco Mele; Nicolas Schneider;

Testing the convergence and the divergence in five Asian countries: from a GMM model to a new Machine Learning algorithm

Abstract

PurposeThe purpose of this paper is to empirically test the economic convergence that operate between five selected Asian countries (namely Thailand, Singapore, Malaysia, the Philippines and Indonesia). In particular, it seeks to investigate how increased economic integration has impacted the inter-country income levels among the five founding members of ASEAN.Design/methodology/approachA new Machine Learning (ML) approach is applied along with a panel data analysis (GMM), and the application of KOF Globalization Index.FindingsThe Generalized Method of Moments (GMM) results highlight that the endogenous growth theory seems to be supported for the selected Asian countries, indicating evidence of diverging forces resulting from unequal growth and polarization dynamics. Overcoming the technical issues raised by the econometric approach, the new ML algorithm brings contrasted but interesting results. Using the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members.Originality/valueUsing the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members. As a matter of fact, the new LSTM algorithm has provided consistent evidence supporting the existence of converging forces. In fact, the results highlighted the effectiveness of the experiments and the algorithm we chose. The high predictability of the authors’ model and the absence of self-alignment in the values showed a convergence be-tween the economies.

Country
Italy
Keywords

convergence, panel data, Machine learning, GMM, LSTM, convergence; ASEAN; GMM; panel data; Machine Learning; LSTM, ASEAN

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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