Quant­it­at­ive Meth­ods

Fagets indhold

This course provides a rigorous introduction to econometric methods used to analyze economic and behavioral data. In contrast to first-year statistics, which focuses on foundational concepts and probability tools, this course emphasizes empirical model building, estimation, interpretation, and causal reasoning in applied research settings.

The course begins with an introduction to empirical analysis and progresses through increasingly advanced econometric models. Students learn the simple and multiple linear regression model; nonlinear and interaction specifications; binary and multinomial outcome models; panel-data methods including fixed and random effects; instrumental variables and the logic of identification; and maximum likelihood estimation. These topics mirror the structure of the lecture plan and are reinforced in hands-on exercise classes working with real-world datasets across a variety of social-science contexts.

Throughout the course, students develop the ability to translate theoretical or institutional questions into testable empirical models, evaluate the assumptions behind different estimators, and interpret results in a conceptually sound way. While the course is broadly applicable to economics, management, public policy, and related fields, occasional examples connect to behavioral finance, illustrating how econometric tools can be used to study systematic patterns in financial decision making.

An integral component of the course is practical data analysis in Stata. Students will learn to implement the methods covered in the lectures, diagnose model limitations, conduct robustness checks, and critically assess empirical claims in academic and applied research. By the end of the course, students will be prepared to execute independent empirical projects and engage with modern econometric evidence at a level well beyond introductory statistics.