Microeconomic Modeling of Consumer Choice Based on Panel Statistical Data

Abstract

The purpose of this article is to substantiate the theoretical foundations and methodological tools of microeconomic modeling of consumer choice using panel statistical data. The study aims to identify relevant types of models suitable for reflecting individual decision-making processes and to determine their applicability for analyzing structured panel datasets that account for both time dynamics and consumer heterogeneity.

The research integrates neoclassical utility theory, behavioral economics, and experimental approaches to build a comprehensive modeling framework. The article applies logit models within the context of panel data structures, incorporating fixed and random effects to capture unobserved individual characteristics. The study uses data from the Ukrainian Household Living Conditions Survey conducted by the State Statistics Service of Ukraine from 2018 to 2023. Statistical processing includes normalization, categorization, imputation of missing values, and econometric specification testing. The estimation is performed through logistic regression with likelihood maximization adapted for panel structures.

The results confirm that logit modeling of consumer choice within panel data enables more accurate predictions of behavioral responses to income changes, price variation, and demographic factors. The panel structure allows separating individual and temporal effects, which improves the robustness of estimated parameters. Moreover, the models reveal statistically significant behavioral patterns and regional disparities in household consumption behavior in times of economic instability.

Microeconomic modeling using panel statistics enhances the explanatory and predictive capacity of consumer choice analysis. The integration of statistical and economic tools enables capturing complex interactions between economic constraints and behavioral tendencies. The study provides a methodological basis for applying logit models to real-world household data, supporting informed decision-making in public policy, targeted social programs, and market segmentation strategies.