Deep learning and machine learning methods are being applied in various ways in economics research, such as predicting economic indicators, forecasting demand, analyzing and predicting financial markets, improving resource allocation, and analyzing and predicting the impact of economic policies. These methods have the potential to provide valuable insights and improve decision-making in various settings. In the future, machine learning and deep learning models that excel at natural language processing (NLP), computer vision, and reinforcement learning are likely to be particularly useful in economics research

Data science is an interdisciplinary field that encompasses a wide range of techniques for analyzing data, including data engineering, data analysis, deep learning, and machine learning. These techniques can be applied to a wide range of fields, including economics, to extract insights and make informed decisions.

Examples of their application in economics research include:

• Forecasting economic indicators
• Analyzing financial markets
• Studying consumer behavior
• Optimizing pricing strategies
• Detecting fraudulent activity
• etc.

With further literature review, it is likely that more details and insights can be gained on the ways in which deep learning and machine learning are being applied to finance and economics.

I am more interested in using machine learning and deep learning to analyze strategic behavior, model social interactions, optimize decision-making, and understand the effects of cooperation and defection in game theory. These techniques can be used to study mutually beneficial outcomes and the effects of unilateral advantage in games, as well as to predict outcomes in games with mixed strategies.

Dear Zishan Mai ,

Machine learning topics started to appear in economic literature on a larger scale in the 1980s when the main concepts such as backpropagation, recurrent neural networks (RNNs) and restricted Boltzmann machines (RBM) were discovered, and topics like computer vision attracted a lot of attention. The increasing trend is visible in the frequency of articles dealing with machine learning published in four leading economic journals over the last couple of decades (see the chart below).

https://towardsdatascience.com/use-of-machine-learning-in-economic-research-what-the-literature-tells-us-28b473f26043

Regards,

Shafagat

Please allow me to pay my respects to you all. Thank you for your valuable reply and detailed information, and wish you a Happy New Year 2023!

In my opinion, empirical research in economics focuses more on the correlation and causality between variables and on the true economic meaning of the variables in the model. I believe that machine learning and deep learning models that can be better applied to economics research in the future will be those models that are more interpretable (e.g., causal convolutional neural networks, etc.), these models should be able to explain the effect of each variable on the economy while performing regression (just like the significance and regression coefficients in traditional econometrics, etc.).

Dear Ramtin Zargari Marandi ,

Thank you for your wonderful answer and the information you provided, and Happy New Year 2023!

Kind Regards,

Zishan

Dear Abderrahmane Boudribila ,

Thank you for your valuable answers and sharing, and I wish you a happy new year 2023!

Kind Regards,

Zishan

Dear U P B Herath ,

Thank you very much for your reply and sharing, and happy new year!

Kind Regards,

Zishan

Dear Shafagat Mahmudova ,

Thank you for your detailed reply and the relevant information shared, which has helped me to understand more about the history of machine learning applications in economics. I wish you a happy new year!

Kind Regards,

Zishan

Zishan Mai You're welcome! I'm happy to help. I wish you a happy new year as well! May the new year bring you joy, good health, and prosperity. If you have any other questions, don't hesitate to ask