METHODOLOGY FOR FORECASTING TAX REVENUE COLLECTIONS UNDER CONDITIONS OF HIGH UNCERTAINTY

Abstract

This article develops a methodological approach for forecasting tax revenues to Ukraine’s budgets under wartime economic conditions characterized by high uncertainty, structural disruptions, and frequent regulatory amendments. The proposed approach is embedded in a coherent macroeconomic scenario, combining an institutional-analytical framework based on tax buoyancy with structured qualitative expert assumptions concerning future tax policy, administration, and external economic dynamics. Unlike purely statistical extrapolation, the model explicitly captures causal relationships among macroeconomic indicators, tax bases, effective tax burdens, and institutional constraints.
The estimation framework links revenue projections to relevant tax bases, including household consumption, payroll, corporate profits, and imports. Forecast values are derived through calibrated effective taxation coefficients accounting for statutory rates, exemptions, compliance gaps, and administrative capacity. A key advantage of the model is its adaptability; parameters can be dynamically revised as legislation changes, sectoral GDP compositions shift, or shadow-economy patterns evolve, thereby improving projection realism for short- and medium-term budget planning horizons.
Empirical testing on wartime data demonstrates robust predictive performance, minimizing forecasting errors relative to actual tax revenues while preserving consistency with major macro-fiscal aggregates. Comparative assessments indicate that the proposed model effectively reflects policy shocks, institutional transformations, and non-linear behavioral effects. Future development pathways include calculation automation, dynamic real-time dashboards, and integration into advanced scenario-based macro-fiscal models for strategic public finance management.