Innovating trade: How high-tech exports, ICT services and R&D expenditure shape global trade patterns with advanced machine learning insights

Purpose This study constructs a fully balanced panel dataset for 135 countries spanning 2013–2022 to explore the determinants of international trade. It employs classical econometric techniques – Robust Least Squares (RLS), Generalized Linear Model (GLM) and quantile regression – to capture linear effects, heterogeneity and distributional nuances. Complementing these, advanced Machine Learning (ML) methods – including Gradient Boosting Machine (GBM), bagging via Random Forest and an ensemble stacking model – uncover nonlinear relationships and complex interactions. All numeric variables are scaled, and a training/testing split is implemented, ensuring robust performance evaluation through metrics such as MAE, MSE, RMSE and R2. Design/methodology/approach Advanced ML techniques are utilized extensively for both regression and robustness checks. For regression, ML methods such as bagging via Random Forest, boosting and stacking with a meta-learner are employed. Findings Empirical evidence from both econometric and ML analyses reveals that a strong business environment (BE), high-tech exports (HTE), robust ICT services imports (ICTSI) and widespread ICT use (ICTU) significantly promote trade intensity across 135 countries from 2013 to 2022. Quantile regressions indicate that HTE’s positive impact intensifies at higher trade quantiles, whereas persistent underinvestment in R&D (RDC) consistently hampers trade performance. Advanced ML models, particularly GBM and ensemble stacking, further capture nonlinearities and interactions, reinforcing these findings and underscoring the critical role of digital infrastructure and innovation ecosystems in driving global trade competitiveness. Originality/value This study uniquely bridges classical econometrics with state-of-the-art ML to examine the trade–innovation nexus. It harnesses a fully balanced panel of 135 countries (2013–2022) and employs RLS, GLM, quantile regression, alongside advanced ML techniques like gradient boosting, bagging via Random Forest and stacking ensembles. This dual approach not only captures both linear and nonlinear dynamics but also enhances predictive accuracy and model interpretability. The integration of these methods sets a novel benchmark, offering robust, data-driven insights and context-specific policy recommendations that enrich the literature on global trade patterns amid rapid technological advancement.