Analyzing the Relationship Between the Inflation Rate and Egypt's Current Account Balance Using Machine Learning During the Period (1991–2023) تحليل العلاقة بين معدل التضخم وميزان الحساب الجاري لمصر باستخدام تقنيات التعلم الآلي خلال الفترة من 1991 إلى 2023

Elseraty, Amr; Elseraty, mohamed

doi:10.21608/mauta.2025.414840.1009

	Analyzing the Relationship Between the Inflation Rate and Egypt's Current Account Balance Using Machine Learning During the Period (1991–2023) تحليل العلاقة بين معدل التضخم وميزان الحساب الجاري لمصر باستخدام تقنيات التعلم الآلي خلال الفترة من 1991 إلى 2023
منارة الاسكندرية للعلوم التجارية
Volume 1, Issue 2, July 2025, Page 181-148 PDF (972.13 K)
Document Type: الدراسات والبحوث العلمية
DOI: 10.21608/mauta.2025.414840.1009
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Authors
Amr Elseraty ¹; mohamed Elseraty²
¹Alexandria
²Faculty of computer science and engineering Alamein international university
Abstract
This study investigates the relationship between inflation rate and current account balance in Egypt over more than three decades (1991-2023), covering three distinct economic reform periods. The research employs a comprehensive suite of advanced machine learning algorithms to explore this relationship, including (SVM), (ANN), (RF), (GB), (DT), and (KNN). The results demonstrate that the Gradient Boosting algorithm achieved superior predictive performance with a coefficient of determination (R² = 0.987), indicating its ability to explain 98.7% of the variance in Egypt's inflation rate. Feature importance analysis revealed that exchange rate fluctuations constitute the most significant determinant of inflation (34.2%), followed by current account balance as a percentage of GDP (28.7%), confirming the study's central hypothesis. The study uncovered distinct patterns in the inflation-current account relationship across different reform periods: a strong inverse relationship during the Structural Adjustment Period (1991-2003), a complex non-linear relationship during the Economic Reform and Development Program (2004-2011), and a dynamic relationship during the recent reform period (2016-2023). The analysis identified a critical threshold of 3% of GDP for current account deficit, beyond which inflationary pressures intensify significantly. . The study's consistent high performance across different reform periods (R² > 0.985) demonstrates the model's adaptability to changing economic conditions and validates its utility for real-time policy analysis and inflation forecasting in Egypt's dynamic economic environment. This research contributes to the economic literature by applying advanced machine learning techniques to long-term Egyptian economic data and provides a methodological framework applicable to other emerging economies.
Keywords
Inflation; Current Account Balance; Machine Learning; Egyptian Economy; Economic Reform


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