Computational Analysis of GDP, Inflation, and Unemployment Trends in the Western Balkans

Session

Computer Science and Communication Engineering

Description

Using computational econometric techniques, this study examines three important macroeconomic indicators over a multi-year period: GDP growth, inflation, and unemployment. The study uses a Python-based analytical pipeline for data processing, visualization, and interpretation by integrating cross-sectional and time-series data from trustworthy sources. The methodology evaluates indicator behavior under different economic conditions by using statis- tical learning and segmentation across multiple temporal phases. Based on the research outcomes, traditional macroeconomic frameworks, such as Okun's Law and the Phillips Curve, exhibit limited predictive accuracy across heterogeneous economic environments, particularly when incorporating the effects of global disruptions. The research presents a modular analytical framework for real-time monitoring of macroeconomic dynamics, emphasizing the necessity for flexible, data-driven policy approaches. By integrating economic theory with computa- tional modeling, the study advances the development of scalable decision-sup- port systems, enhancing resilient governance in environments characterized by uncertainty, structural heterogeneity, and exogenous volatility.

Keywords:

GDP, Inflation, Unemployment, Comparative Analysis, Economic Indicators

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-982-41-2

Location

UBT Lipjan, Kosovo

Start Date

25-10-2025 9:00 AM

End Date

26-10-2025 6:00 PM

DOI

10.33107/ubt-ic.2025.102

This document is currently not available here.

Share

COinS
 
Oct 25th, 9:00 AM Oct 26th, 6:00 PM

Computational Analysis of GDP, Inflation, and Unemployment Trends in the Western Balkans

UBT Lipjan, Kosovo

Using computational econometric techniques, this study examines three important macroeconomic indicators over a multi-year period: GDP growth, inflation, and unemployment. The study uses a Python-based analytical pipeline for data processing, visualization, and interpretation by integrating cross-sectional and time-series data from trustworthy sources. The methodology evaluates indicator behavior under different economic conditions by using statis- tical learning and segmentation across multiple temporal phases. Based on the research outcomes, traditional macroeconomic frameworks, such as Okun's Law and the Phillips Curve, exhibit limited predictive accuracy across heterogeneous economic environments, particularly when incorporating the effects of global disruptions. The research presents a modular analytical framework for real-time monitoring of macroeconomic dynamics, emphasizing the necessity for flexible, data-driven policy approaches. By integrating economic theory with computa- tional modeling, the study advances the development of scalable decision-sup- port systems, enhancing resilient governance in environments characterized by uncertainty, structural heterogeneity, and exogenous volatility.