Description:

This Econometrics course is designed for Master of Science students to develop a rigorous understanding of empirical methods used in modern economic analysis. The course begins with an introduction to the goals and logic of econometric modeling, emphasizing the distinction between correlation and causation. It proceeds to cover regression and causal inference, laying the theoretical foundations for understanding how models estimate relationships between variables. Students then explore linear regression, the core tool of econometrics, followed by an in-depth examination of the properties of ordinary least squares (OLS) estimators in both small and large samples, including bias, consistency, and efficiency. The section on inference focuses on hypothesis testing, confidence intervals, and p-values in econometric contexts. The course then addresses instrumental variable estimation, a key technique for dealing with endogeneity and causal identification. Finally, a summary and synthesis section integrates all topics, with practical applications using statistical software, real-world data exercises, and replication of empirical research. By the end of the course, students will be able to critically evaluate econometric studies, design their own empirical research, and apply advanced regression methods to policy and economic questions.

The Lecture is delivered online