The Role of Data Science in Modern Economic Forecasting
Downloads
Keywords:
Data Science, Economic Forecasting, Machine Learning, Artificial Intelligence, Predictive ModelingAbstract
This article examines how data science, through machine learning (ML) and artificial intelligence (AI), is revolutionizing economic forecasting. Traditional econometric models, often linear and simplistic, fail to capture complex economic dynamics. Data science, by leveraging vast datasets and advanced algorithms, offers more accurate forecasts for critical indicators such as inflation, unemployment rates, and GDP growth. This paper highlights key use cases of AI-driven models and discusses how they are transforming economic analysis and decision-making.
Downloads
References
Blount, J.J., D.R. Tauritz, and S.A. Mulder. (2011) Adaptive Rule-Based Malware Detection Employing Learning Classifier Systems: A Proof of Concept. in Computer Software and Applications Conference Workshops (COMPSACW), 2011 IEEE 35th Annual. 2011.
Box, G.E.P., and G.M. Jenkins. (1970) Time Series Analysis: Forecasting and Control. Holden-Day. San Francisco.
Chatfield, C. (2003) The Analysis of Time Series: An Introduction. CRC Press.
Google AI Blog. (2020) Predicting Inflation with Search Query Data. https://ai.googleblog.com/2020/07/predicting-inflation-search-data.html.
Federal Reserve Board. (2021) AI in Economic Forecasting: The Case for Hybrid Models. https://www.federalreserve.gov/ai-economic-forecasting.pdf.
Bishop, C.M. (2006) Pattern Recognition and Machine Learning. Springer.
Smola, A.J., and B. Schölkopf. (2004) A Tutorial on Support Vector Regression. Statistics and Computing, 14(3): 199-222.
Sims, C.A., and T. Zha. (1999) Error Bands for Impulse Responses. Econometrica, 67(5): 1113-1155.
Varian, H.R. (2014) Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2): 3-28.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
Plaudit
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.