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

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.