Document Type
Article
Publication Date
1-1-2024
Journal / Book Title
International Journal of Business Analytics
Abstract
The United States federal debt has witnessed a significant surge over recent decades. This study delves into inquiries regarding the persistent patterns in federal debt, key factors driving this alarming trend, and the optimal timing for implementing corrective measures to mitigate its speeding flight. Utilizing modern machine learning techniques, notably Random Forest (RF) and Support Vector Regression (SVR), alongside conventional statistical forecasting techniques, the research aims to predict future trends. It emphasizes the critical role of business analytic thinking in deciphering fiscal system-based complexities. To address the mounting challenges, these research findings underscore the urgent necessity for efficacious policies to oversee them.
DOI
10.4018/IJBAN.360380
MSU Digital Commons Citation
Wang, John; Jain, Arti; Yadav, Arun Kumar; and Yadav, Divakar, "Analyzing the Complexity of US Federal Debt: A Mathematical Approach" (2024). Department of Information Management and Business Analytics Faculty Scholarship and Creative Works. 174.
https://digitalcommons.montclair.edu/infomgmt-busanalytics-facpubs/174
Rights
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Published Citation
Wang, J., Jain, A., Yadav, A. K., & Yadav, D. (2024). Analyzing the complexity of US federal debt: a mathematical approach. International Journal of Business Analytics (IJBAN), 11(1), 1-22.