• Define and interpret key econometric concepts, including estimator, estimation, estimate, identification, and causality, and explain their roles in empirical analysis
• Identify key challenges to causal inference in a given research setting (e.g., omitted variables, selection bias, reverse causality), and evaluate the strengths, limitations, and feasibility of alternative identification strategies given the available data.
• Select and justify an appropriate econometric model and identification strategy for a specific research question, taking into account data limitations and the assumptions required for causal interpretation.
• Estimate econometric models using STATA, ensuring correct implementation of commands and procedures.
• Interpret and evaluate estimation results from STATA output, including coefficients, standard errors, statistical significance.
• Link econometric theory to practice by clearly connecting model assumptions, STATA code, and empirical results.
• Present empirical findings clearly and appropriately, assessing whether results are reported and communicated in a meaningful way.

This course builds on a standard introductory course in econometrics. Students are expected to have knowledge of fundamental concepts in mathematics and statistics and to be familiar with matrix notation.

The aim of the course is to develop both a theoretical and applied understanding of econometric models and estimation methods. The course emphasizes what can and cannot be concluded from empirical analysis, how to identify limitations and potential shortcomings of different approaches, and how to distinguish between causal and non-causal relationships. Students will learn to assess plausible identification strategies for causal inference given the available data and its constraints.

The course covers both cross-sectional and panel data, with applications involving continuous and binary dependent variables.

Each topic is introduced in technical terms and followed by exercises and applied examples. Lectures combine traditional instruction with short individual or group exercises to reinforce key concepts. Exercise sessions provide opportunities for more in-depth practice on problems not covered in lectures, with students working individually or in groups, followed by class discussions and explanations.

• Classic and basic theory
• Methodology
• Models

• Analysis
• Discussion, critical reflection, modelling

Part of this course may also be taken as a PhD course for a limited number of PhD students.

Indicative:

Lectures:

• Lecture notes

Textbooks:

• Wooldridge, J. (2020), "Introductory Econometrics", 7th edition, Cengage,

  ISBN10: 1-337-55886-9

• Jeffrey Wooldridge (2010), "Econometric Analysis of Cross Section and Panel Data", 2nd ed, MIT Press