Data Science in Economics
It provides a systematic review for researchers and practitioners in economics, but it is incremental as it summarizes existing trends without new methods or data.
This paper surveys the state of the art of data science in economics, finding that hybrid models are the most common (over 51% of reviewed articles) and have higher prediction accuracy based on RMSE metrics.
This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.