Course 2026-2027 a.y.

• Introduction to econometrics. (What is Econometrics?; Steps in empirical economic analysis; The structure of economic data; Causality and the notion of ceteris paribus in econometric analysis)
• Simple regression model. (Definition; Multiple ways of deriving the Ordinary Least Squares (OLS) estimates; Properties of OLS in any sample of data)
• Multiple regression analysis: Estimation. (Motivation for multiple regression; Mechanics and interpretation of OLS; Expected value and variance of OLS estimator; Gauss-Markov Theorem and Efficiency of OLS estimator)
• Multiple regression analysis: Inference and heteroskedasticity. (Distribution of OLS estimator in finite samples)
• Confidence intervals; Tests of simple and multiple hypotheses about population parameters)
• Multiple regression analysis: OLS asymptotics. (Properties of OLS estimator in infinite samples)
• Multiple regression analysis with qualitative information: Dummy variables. (Use of dummy explanatory variables and their interactions to the aims of incorporating qualitative or ordinal information in regression analysis and of performing tests of hypothesis and policy analysis involving comparisons of groups. Dummy dependent variables and the linear probability model)
• Pooling cross sections across time: Simple panel data methods. (Differences-in-Differences; First Differencing)
• Instrumental variables (IV) estimation and Two stages least squares (2SLS)
• [Time permitting] Limited dependent variable (LDV) models with binary dependent variables. (Logit and Probit; Maximum Likelihood Estimation)

• Define key concepts in econometrics, for instance “econometric model”; “random sample;” “ceteris paribus;” “counterfactual;” “causal effect;” “exogeneity/endogeneity;” “homoskedasticity/heteroskedasticity;” “restricted/unrestricted model;” “finite-sample/asymptotic bias;” “omitted variables bias;” “dummy variable trap.”
• Explain key differences and links between distinct but related econometric concepts, for instance “econometric model and estimation method;” “population and sample;” “population parameter, estimator, and estimate;” “correlation and causal effect;” “error term and residual;” “point estimate and confidence interval;” “significance level and critical value;” “proxy variable and instrumental variable.”
• Recognize different types of econometric data among: cross sections, time series, pooled cross sections, and panel/longitudinal data. Explain their differences and similarities.
• State the assumptions of the classical linear regression model and explain their roles within the Gauss-Markov Theorem. Name and discuss the main consequence(s) of the failure of each assumption, illustrating with specific microeconomic examples or applications.
• Describe different ways of deriving and interpreting the OLS estimators (estimates) for the parameters of a linear regression model, among: Minimizing the sum of the squared residuals; Method of moments & Sample analog principle; Maximizing the likelihood function.  
• Discuss the advantages and disadvantages of different econometric strategies used to identify and estimate causal effects, including: Differences-in-Differences; First differences; Instrumental Variables.
• Interpret the empirical findings reported in published applied research, including regression tables, identification strategies, and the connection between economic theory and the corresponding estimation.

• Apply simple and multiple regression analysis to quantify relationships among economic variables of interest and to test simple and joint hypotheses about such relationships.
• Assess the statistical and economic significance of estimated relationships among economic variables.
• Perform simple analysis of bias to assess whether estimated relationships among economic variables may be interpreted as ceteris paribus (causal) effects and, if not, to assess the likely direction of the bias. 
• Interpret and critically assess empirical findings presented by others based on regression analysis of economic data.
• Incorporate dummy variables and their interactions into regression analysis to test stability of regression parameters across different groups or time periods.
• Choose and apply the appropriate econometric strategy among those covered in class (e.g., Differences-in-Differences, First Differences, Instrumental Variables) to quantify causal effects among economic variables, taking in to account the characteristic features of the application and of the available data.
• Evaluate the causal effects of policy interventions/programs using simple econometric tools such as Differences-in-Differences or Instrumental Variables methods.
• Work collaboratively in a small team to design, conduct, and present a short empirical analysis on real data, communicating findings clearly to a non-specialist audience.
• Conduct empirical analysis of real economic data using R, including data import, descriptive statistics, regression estimation, hypothesis testing, and presentation of results in standard table and graphical formats.

• Lectures
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments
• Interaction/Gamification

The learning experience of the course includes:

1. Face-to-face lectures, introducing and illustrating the main topics of the course. Each lecture opens with a published empirical paper that motivates the methodology and closes with a hands-on data analysis on that paper's own dataset, executed live in R, so that theory and application are presented together rather than in separate sessions.
2. Interactive in-class discussions around the empirical applications presented at the end of each lecture, particularly on specific aspects of model specification, identification, and interpretation, where students are encouraged to bring their own contributions and share their insights.
3. Weekly not-for-credit problem sets assigned throughout the semester. Each problem set extends the same paper and dataset featured in the corresponding lecture, includes an R coding exercise, and is calibrated to the difficulty expected on the exam. Students can choose freely whether to work individually or with classmates.
4. Collective review sessions before each partial exam, devoted to the in-class solution of problem-set exercises previously assigned to students. These give students the opportunity to consolidate the methodological and analytical tools covered in the course on a variety of microeconomic and policy-relevant applications.
5. Hands-on use of statistical software (R) integrated throughout all lectures and problem sets. R is open-source, widely used in academia and industry, and is introduced progressively, with starter templates and worked examples provided.
6. Group video project on real economic data. Teams of 3–5 students select from a curated menu of empirical datasets, conduct an analysis, and produce a short (max 5-minute) video presenting their findings. The project develops teamwork, structured engagement with real data across the semester, and the ability to communicate empirical results clearly.
7. "Oscar Night" peer evaluation session in the final lecture, where the whole class watches the videos together and evaluates them collectively, providing peer feedback on both the analytical content and the communication.
8. Video production workshop delivered by BUILT (Week 5), supporting students with storyboarding, filming, and editing skills for the group video project.

Attending students participate in the collaborative group video project, which accounts for 15% of the final grade.

Grading is based on two possible routes:

• Route A — Partial exams: First partial written exam (50%) + Second partial written exam (50%) = 100%
• Route B — General exam: Single written general exam (100%) = 100%

The two partial written exams and the general written exam all consist of a mix of open questions, multiple-choice questions, and applied exercises. They test students' understanding of the main microeconomic principles covered in the course, including consumer and firm decision-making, competitive equilibrium and market failures, market power, externalities and public goods, asymmetric information, and platform competition; their ability to analyze how different competitive conditions affect market outcomes; and their ability to apply microeconomic reasoning to contemporary economic challenges, including those raised by the digital economy. The general exam covers the entire course program.

The students can get up to 1 extra point for participating in the group video project, which is conducted in teams of 4–5 students. Each team selects a topic from a curated list of digital economy themes, develops a short (max 3/4-minute -- length to be decided in class) video presenting their analyses of an applied econometric problem from a list of possible datasets, and submits the final video for the in-class "Oscar Night" session. The project assesses students' ability to apply rigorous econometric concepts to economic questions, to work collaboratively in a small team, and to communicate the resulting analysis clearly to a non-specialist audience. 

Evaluation combines the assessment of the analytical content of the work and peer evaluation of the videos during the Oscar Night session.

The Oscar Night grade is available only for students passing either both partial exams or the final exam by the end of the academic year.

The main course material for both attending and non-attending students is:

• J.M. WOOLDRIDGE, Introductory Econometrics: A Modern Approach (2012 or any subsequent edition).
• The slides of the course, problem sets, and additional support material are uploaded to the BBoard platform of the course.