GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest

2025-04-29

AI Summary

This study investigates the nonlinear relationship and regional heterogeneity between GDP growth and gross fixed capital formation (GFCF) across advanced (G7, EU-15, OECD) and emerging (BRICS) economies. Methodologically, it integrates panel linear regression with an enhanced random forest model and proposes a novel parallelized *p*-value-based importance algorithm to quantify variable contributions; structural breaks are identified via sequential probability ratio tests (SPRT) and sequential two-sample tests (SAPT). Results challenge the conventional “GDP-driven investment” hypothesis, revealing lagged GFCF as the primary investment determinant. Taxation and unemployment exhibit pronounced regional divergence: GDP–GFCF coupling is stronger in advanced economies, whereas BRICS economies show heightened sensitivity to unemployment. The hybrid modeling framework improves investment forecasting accuracy and delivers empirically grounded insights for designing differentiated fiscal and industrial policies.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

This study examines the relationship between GDP growth and Gross Fixed Capital Formation (GFCF) across developed economies (G7, EU-15, OECD) and emerging markets (BRICS). We integrate Random Forest machine learning (non-linear regression) with traditional econometric models (linear regression) to better capture non-linear interactions in investment analysis. Our findings reveal that while GDP growth positively influences corporate investment, its impact varies significantly by region. Developed economies show stronger GDP-GFCF linkages due to stable financial systems, while emerging markets demonstrate weaker connections due to economic heterogeneity and structural constraints. Random Forest models indicate that GDP growth's importance is lower than suggested by traditional econometrics, with lagged GFCF emerging as the dominant predictor-confirming investment follows path-dependent patterns rather than short-term GDP fluctuations. Regional variations in investment drivers are substantial: taxation significantly influences developed economies but minimally affects BRICS, while unemployment strongly drives investment in BRICS but less so elsewhere. We introduce a parallelized p-value importance algorithm for Random Forest that enhances computational efficiency while maintaining statistical rigor through sequential testing methods (SPRT and SAPT). The research demonstrates that hybrid methodologies combining machine learning with econometric techniques provide more nuanced understanding of investment dynamics, supporting region-specific policy design and improving forecasting accuracy.

Problem

• Examines GDP-GFCF relationship across developed and emerging economies
• Compares traditional econometrics with machine learning for investment analysis
• Identifies regional variations in GDP's influence on corporate investment

Innovation

• Combines Random Forest with traditional econometric models
• Introduces parallelized p-value importance algorithm
• Uses hybrid methodologies for nuanced investment analysis