The aim of this course is to enable students to understand the structure of panel data, the main assumptions of panel data models, and the basic stages of applied panel data analysis. Throughout the course, students will gain knowledge and practical skills in constructing and organizing panel datasets, pooled ordinary least squares, fixed effects models, random effects models, model selection tests, basic diagnostic tests, robust standard errors, introduction to dynamic panel data models, and reporting empirical results.

Panel Data Analysis focuses on the econometric analysis of datasets that combine cross-sectional and time dimensions. The course covers the preparation of panel datasets, pooled OLS, fixed effects, random effects, model selection, diagnostic tests, robust standard errors, introduction to dynamic panel data models, and empirical reporting processes.