Economic Modelling at thirty-five: A retrospective bibliometric survey

Debidutta Pattnaik (), Satish Kumar, Bruce Burton and Weng Marc Lim

Economic Modelling, 2022, vol. 107, issue C

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.

Keywords: Economic modelling; Bibliometrics; Machine learning; N-gram analysis; Co-occurrence analysis (search for similar items in EconPapers)
JEL-codes: C00 E44 F31 N01 O1 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:107:y:2022:i:c:s0264999321003011

DOI: 10.1016/j.econmod.2021.105712

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