Economic Modelling at thirty-five: A retrospective bibliometric survey

Debidutta Pattnaik; Satish Kumar; Bruce Burton; Weng Marc Lim

doi:10.1016/j.econmod.2021.105712

Economic Modelling at thirty-five: A retrospective bibliometric survey

Debidutta Pattnaik, Satish Kumar, Bruce Burton, Weng Marc Lim

Research output: Contribution to journal › Article › peer-review

31 Citations (Scopus)

238 Downloads (Pure)

Abstract

Economic modelling (EM) is a premier journal for policy-relevant economic models. However, so far, no retrospective studies exist for the journal. This study addresses this gap using a machine learning n-gram (bigram and trigram) analysis. The survey results find that the journal has contributed to 9517 topics, with 69 topics covered in at least 10 studies between 1984 and 2019. Through a co-occurrence analysis of bigram and trigram terms, this study reveals that the major topics in the journal converge to nine themes: international economics, development economics, regional and real estate economics, economic growth and development, financial economics, monetary economics, general economic equilibrium, international finance, and non-conventional finance and macroeconomics. This study concludes with key takeaways and suggestions for prospective authors intending to publish their best papers in EM.

Original language	English
Article number	105712
Number of pages	15
Journal	Economic Modelling
Volume	107
Early online date	19 Nov 2021
DOIs	https://doi.org/10.1016/j.econmod.2021.105712
Publication status	Published - Feb 2022

Keywords

Economic Modelling
bibliometrics
machine learning
n-gram analysis
co-occurrence analysis
N-gram analysis
Bibliometrics
Machine learning
Economic modelling
Co-occurrence analysis

ASJC Scopus subject areas

Economics and Econometrics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.econmod.2021.105712Licence: Elsevier User Licence

Author Accepted ManuscriptAccepted author manuscript, 928 KBLicence: CC BY-NC-ND

Cite this

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abstract = "Economic modelling (EM) is a premier journal for policy-relevant economic models. However, so far, no retrospective studies exist for the journal. This study addresses this gap using a machine learning n-gram (bigram and trigram) analysis. The survey results find that the journal has contributed to 9517 topics, with 69 topics covered in at least 10 studies between 1984 and 2019. Through a co-occurrence analysis of bigram and trigram terms, this study reveals that the major topics in the journal converge to nine themes: international economics, development economics, regional and real estate economics, economic growth and development, financial economics, monetary economics, general economic equilibrium, international finance, and non-conventional finance and macroeconomics. This study concludes with key takeaways and suggestions for prospective authors intending to publish their best papers in EM.",

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AU - Burton, Bruce

AU - Lim, Weng Marc

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KW - bibliometrics

KW - machine learning

KW - n-gram analysis

KW - co-occurrence analysis

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Economic Modelling at thirty-five: A retrospective bibliometric survey

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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