APPLIED ECONOMETRICS: THEORETICAL FOUNDATIONS, ESTIMATION TECHNIQUES, AND EMPIRICAL APPLICATIONS

• Djumanazarova Zamira Kojaboyevna

  Senior Lecturer, Department of Business and Management, Oriental University, Tashkent, Uzbekistan

  Author

Applied econometrics bridges economic theory and empirical data, equipping researchers with rigorous statistical tools for causal inference, forecasting, and policy evaluation. This article provides a systematic overview of the core methods constituting the applied econometrics toolkit: Ordinary Least Squares (OLS) regression and its classical assumptions, panel data estimators (pooled OLS, fixed effects, and random effects), instrumental variable and two-stage least squares (IV/2SLS) estimation, time-series analysis including unit-root and cointegration tests, robust and cluster-robust inference, and dynamic panel GMM methods. For each technique the theoretical motivation, identifying assumptions, diagnostic procedures, and remedies for assumption violations are presented. The article further synthesises selected empirical applications drawn from development economics, household finance, and transition economy contexts — with particular reference to the determinants of household savings in Uzbekistan (Djumanazarova, 2025) — illustrating how methodological choices shape substantive conclusions. Cross-tabular comparisons of estimator properties and a practical implementation guide facilitate both classroom instruction and independent research.1 The discussion concludes with a forward-looking assessment of emerging frontiers: machine learning augmentation of econometric models, causal forests, and synthetic control methods.

• pdf

This work is licensed under a Creative Commons Attribution 4.0 International License.