Economic Modelling at thirty-five: A retrospective bibliometric survey

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

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
132 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 languageEnglish
Article number105712
Number of pages15
JournalEconomic Modelling
Volume107
Early online date19 Nov 2021
DOIs
Publication statusPublished - 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

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