Atıf Sayısı
2
Görüntüleme
57
İndirme
21
Amaç- Bu çalışmada 2010 – 2019 yılları arasındaki son on yıllık süreçte Web of Science veri tabanında ekonomi ve ekonometri alanında makine öğrenmesi üzerine yapılan uluslararası nitelikteki çalışmaların ilgili literatürdeki gelişimi Bibliyometrik Analiz ile incelenmiştir. Ardından elde edilen bulgular Sosyal Ağ Analizi uygulanarak görselleştirilmiştir. Metodoloji- Bibliyometrik Analiz, bir disiplin hakkında bilgi toplamak bilimsel verimliliğin ve gelişimin ölçülmesi amacıyla kullanılmaktadır. Sosyal ağ analizi ise bibliyometrik analiz sonucunda elde edilen bulguları görselleştirerek, aralarındaki ilişki yapısını göstermektedir. Bulgular- Bibliyometrik Analiz sonucunda, yıllar bazında çalışmalarda meydana gelen değişimler, ilgili konu hakkında en çok yayın yapan yazar ve en fazla atıf alan çalışmalar, bu çalışmaların ülkeler ve kurumlara göre dağılımı, çalışma konusunun seyri ve anahtar kelimelerdeki ilişkisel oluşumdaki ilerlemeler gibi değişkenler temelinde bulgular elde edilmiştir. Ayrıca Sosyal Ağ Analizi ile birbirleriyle ağ yapısı oluşturan yazarlar, ülkeler ve anahtar kelimeler arasındaki kümelenmeler ve ilişkiler haritalandırılmıştır. Sonuç- Bulgular incelendiğinde, bu konular üzerine her yıl en fazla çalışmanın Amerika Birleşik Devletlerinde yapıldığı, en çok atıf alan çalışmanın Varian (2014) olduğu belirlenmiştir. Aynı zamanda, son on yıldaki çalışmalar değerlendirildiğinde, Ekonomi ve Ekonometri’de günümüzde dikkat çeken Makine öğrenmesi konularına yönelimin hızla artmış olduğu, çalışmaların ‘‘nedensellik’’ ve ‘’nedensel etki’’ anahtar kelimelerinin yanısıra ‘‘Finansal Ekonometri’’, ‘’Davranışsal Ekonomi’’, ‘’Deneysel Ekonomi’’ ile de ilişkilendirildiği ve finans ile bağlantılı anahtar kelimelerin sıklıkla kullanıldığı gözlemlenmiştir. Sonuçta elde edilen bulgular Makine Öğrenmesi’nin Ekonomi ve Ekonometri alanlarındaki gelişimine ışık tutar niteliktedir.
Purpose- This study has studied the development of international-quality studies on machine learning in the field of economics and econometrics in the Web of Science database in the last decade between 2010 and 2019 by Bibliometric Analysis. The findings obtained then were visualized by implementing Social Network Analysis. Methodology- Bibliometric Analysis is used to collect information about a discipline; to measure scientific efficiency and development. The social network analysis, by visualizing the findings obtained by the bibliometric analysis, shows the structure of the relationship between them. The findings- Bibliometric Analysis results on the basis of variables such as the changes that occurred in the studies over the years, the writers most published on the subject and the most reference field studies, the distribution of these studies according to countries and institutions, the course of the subject of the study and the progress in the relationship formation in keywords. The Social Network Analysis also maps the accumulations and relationships between the writers, countries and keywords that form a network structure with each other. Results- When the findings are studied, it is determined that the most reference field study on these topics is Varian (2014) which is the most studied yearly in the United States. At the same time, when the studies of the last decade were evaluated, the focus on the topics of machine learning that are today attracted in Economics and Economics has been rapidly increased, the study’s keywords “substantiality” and “substantial impact” are also linked to “Financial Economics”, “Behavioral Economics”, “Experimental Economics” and the keywords related to finance are frequently used. Finally, the findings obtained are the quality of keeping light on the development of machine learning in the fields of economics and econometrics.
Purpose- The purpose of the study, is to examine the development of international studies on machine learning in the field of economics and econometrics using Bibliometric Analysis and to visualize by Social Network Analysis between 2010 and 2019 in the Web of Science database. Methodology- Bibliometric Analysis is used to gather information about a discipline and to measure scientific productivity and progress. Social network analysis shows the relationship structure by visualizing the findings obtained as a result of bibliometric analysis. Findings- As a result of the bibliometric analysis, findings were obtained on the basis of variables such as the changes in the studies on the basis of years, the most published authors and the most cited studies, the distribution of these studies by countries and institutions, the course of the study topic, and the progress in the relational formation in the keywords. In addition, with the Social Network Analysis, the authors that network with each other and clusters and relationships between countries and keywords were mapped. Conclusion- When the findings were examined, it was determined that the most studies on these topics were conducted in the United States of America in each year and the most cited study was Varian (2014). At the same time, when the studies are evaluated in the last decade, it has been observed that the tendency towards the subjects of Machine learning, which attracts attention in Economics and Econometrics, has increased rapidly. It has been observed that the studies are associated with the keywords "causality" and "causal effect" as well as "Financial Econometrics", "Behavioral Economics", "Experimental Economics". The findings obtained shed light on the development of Machine Learning in the fields of Economics and Econometrics.
Alan : Sosyal, Beşeri ve İdari Bilimler
Dergi Türü : Ulusal
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