Çağlayan Akay Ebru, Yılmaz Soydan Naciye Tuba, Kocarık Gacar Burcu
Department of Econometrics, Marmara University, Istanbul, Turkey.
Department of Econometrics, Dokuz Eylul University, Izmir, Turkey.
Soc Netw Anal Min. 2022;12(1):109. doi: 10.1007/s13278-022-00916-6. Epub 2022 Aug 10.
An extensive literature providing information on published materials in machine learning exists. However, machine learning is still a rather new concept in the fields of economics and econometrics. This study aims to identify different properties of published documents about machine learning in economics and econometrics and therefore to draw a detailed picture of recent publications from bibliometric analysis perspectives. For the aim of the study, the data are collected from the publications indexed by Web of Science and Scopus databases from the period 1991 to 2020. Inthe study, the data have been illustrated by VOSviewer for science mapping. The analysis of variance has also been used to identify the links between the number of citations of articles and years. The findings obtained provides information about the studies on machine learning in the relevant field conducted in the past, as well as providing an opportunity to gain knowledge about the researched area by shedding light on what the future research areas would be. There is no doubt that it attracts attention has increased significantly on machine learning in the field of economics and econometrics and academic publications on machine learning in the relevant field have increased over the last decade.
关于机器学习领域已发表材料的文献极为丰富。然而，机器学习在经济学和计量经济学领域仍是一个相对较新的概念。本研究旨在识别经济学和计量经济学中已发表的关于机器学习的文献的不同特性，从而从文献计量分析的角度描绘近期出版物的详细情况。为实现该研究目的，数据收集自科学网（Web of Science）和Scopus数据库在1991年至2020年期间索引的出版物。在本研究中，数据已通过VOSviewer进行科学绘图展示。方差分析也被用于确定文章被引次数与年份之间的联系。所获得的研究结果不仅提供了过去在相关领域开展的关于机器学习研究的信息，还通过揭示未来研究领域是什么，为了解该研究领域提供了机会。毫无疑问，在经济学和计量经济学领域，机器学习已显著吸引了更多关注，并且在过去十年中，相关领域关于机器学习的学术出版物有所增加。
