A Novel Methodological Framework for Quantifying Economic Resilience through Non-Linear Regression Models

Authors

  • Jordan Hill PhD
  • Noah Martin Associate Professor
  • Morgan Thompson D.Sc

Keywords:

economic resilience, non-linear regression, policy analysis, emerging economies, quantitative economics, structural dynamics, data analytics

Abstract

In light of the increasing prevalence of economic shocks and their adverse effects on global markets, this study investigates the importance of economic resilience as a key indicator of a country's ability to withstand such shocks. Utilizing a non-linear regression model, we analyze empirical data spanning two decades across various economies to quantify resilience and identify significant determinants. Our method integrates econometric techniques with advanced data analytics, emphasizing the role of policy interventions and structural characteristics. The findings reveal critical insights into the resilience factors of emerging economies, highlighting how structural disparities influence recovery trajectories. Moreover, this research contributes to the discourse on sustainable economic policies, offering actionable recommendations for policymakers aimed at enhancing economic stability during crises.

Author Biographies

Jordan Hill, PhD

PhD
Harvard University
Massachusetts Hall, Cambridge, MA 02138, USA

Noah Martin, Associate Professor

Associate Professor
University of Oxford
Wellington Square, Oxford OX1 2JD, UK

Morgan Thompson, D.Sc

D.Sc
Ludwig Maximilian University of Munich
Geschwister-Scholl-Platz 1, 80539 München, Germany

References

Adeoye, Y., Adesiyan, K. T., Olalemi, A. A., Ogunyankinnu, T., Osunkanmibi, A. A., & Egbemhenghe, J. (2025). Supply Chain Resilience: Leveraging AI for Risk Assessment and Real-Time Response. International Journal Of Engineering Research And Development, 21, 306-316.

Chinonyerem, C. A., Olalemi, A. A., Paul, M., Nwabunike, O. T., Eniola, O. S., Benjamin, A. O., ... & Seigha, I. B. (2025). Leveraging Machine Learning and Data Analytics to Predict Corporate Financial Distress and Bankruptcy in the United States. Asian Journal of Advanced Research and Reports, 19(6), 65-78.

Published

2025-10-15

Issue

Section

Articles