Understand the structure, advantages, and limitations of panel datasets relative to pure cross-sectional or time-series data. Derive and implement the Pooled OLS, Fixed Effects (Within), and Random Effects (GLS) estimators and explain the assumptions required for their consistency. Apply the Hausman specification test and related diagnostics to select between competing panel estimators. Detect and correct for heteroscedasticity, autocorrelation, and cross-sectional dependence in panel regression models. Estimate dynamic panel data models using the Arellano-Bond GMM estimator and related system GMM approaches. Extend panel methods to binary and limited dependent variable settings using panel Logit and Probit models. Critically evaluate the identification strategies and panel data methods employed in published empirical studies.

Panel Data Analysis provides a perspective of econometric methods designed for datasets that combine cross-sectional and time-series dimensions. The course covers the statistical theory, estimation, and inference of panel data models, including fixed effects, random effects, dynamic panels, and non-linear specifications. Particular attention is paid to the identification and control of unobserved heterogeneity, endogeneity, and serial correlation. Students will acquire both the theoretical foundations and the practical skills to implement advanced panel estimators using econometric software, and to critically evaluate the use of panel methods in applied economic research.

Lecture-based teaching

Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. Arellano, M. (2003). Panel Data Econometrics. Oxford University Press. Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.