A Decade of Applied Quantitative Analytics for Philippine Policy: Forecasting, Statistical Forensics, and Predictive Modeling Across Education, Energy, Agriculture, Health, and Finance

Quantitative analytics has assumed a growing role in Philippine policy research as government and sectoral databases have become increasingly central to planning, monitoring, and resource allocation. This review synthesizes recent work in education, energy, agriculture, health, and finance to examine how forecasting, statistical forensics, and predictive modeling have been applied to Philippine policy problems. Across these sectors, the literature shows a clear methodological progression from descriptive diagnostics and classical time-series models toward comparative machine learning, deep learning, explainable artificial intelligence, nonlinear embedding, and Benford-based anomaly detection. Several recurring strengths emerge, particularly the consistent use of official Philippine datasets, transparent model benchmarking, and close alignment with practical policy concerns such as dropout reduction, electricity and crop planning, disease surveillance, and financial forecasting. At the same time, important limitations remain, including limited multivariate and spatial modeling, uneven validation practices, and relatively little attention to uncertainty quantification and operational deployment. In comparison with the broader international literature, the strongest contributions are those that position analytics as a support tool for planning and monitoring, while the main gaps lie in external validation, richer explanatory structures, and decision-oriented system integration. The evidence suggests that the next phase of Philippine policy analytics should move beyond isolated single-series applications toward integrated frameworks that combine forecasting, data-quality assessment, explainable modeling, and sector-specific decision thresholds for routine governance.