ECONOMETRIC MODELING OF NET INCOME FORMATION OF AN ENTERPRISE UNDER MARTIAL LAW

Abstract

The functioning of economic entities under martial law conditions is characterized by unprecedented volatility, systemic risks, and radical transformation of operational processes. Traditional financial management approaches based on historical patterns and intuitive decision-making demonstrate insufficient effectiveness in such an unstable environment. This creates an urgent need for implementing quantitative analytical instruments capable of formalizing relationships between key financial indicators and providing objective grounds for managerial decisions. The relevance of this research stems from the necessity to adapt financial planning mechanisms to extreme macroeconomic shocks, where liquidity management and short-term financing become critical determinants of enterprise survival. Particular attention is paid to methodological aspects of model specification, including variable identification, residual diagnostics, and verification of statistical assumptions. The research examines how current assets and trade payables interact within a constrained resource environment, highlighting the transformation of commercial credit from a routine liability into a strategic liquidity buffer. Emphasis is placed on the procedural stages of regression analysis, outlier detection, and stability assessment of parameter estimates. By integrating quantitative modeling into anti-crisis management, enterprises can enhance their capacity to forecast income fluctuations, optimize resource allocation, and maintain operational continuity despite external disruptions. The proposed analytical approach serves as a foundation for developing flexible financial planning tools that align short-term operational adjustments with long-term strategic objectives in high-uncertainty environments, ensuring sustainable economic resilience. Furthermore, the methodological design prioritizes transparency and reproducibility, enabling practitioners to validate model adequacy through standardized diagnostic procedures before deploying forecasts in real-world decision-making contexts.