The econometrics teaching program is offered in a cycle over 3 terms. In Econometrics I, the foundations of the subject are dealt with: causality, correlation, assumptions of the linear regression model, OLS estimation, asymptotic tests, misspecification, outliers, heteroskedasticity and an introduction to R. In Econometrics II, advanced subjects are covered: Time series analysis, endogeneity, instrumental variable estimation, panel data and limited dependent variable models. In Applied Econometrics, a deeper analysis of selected topics is offered and students are required to write an empirical, applied-econometric essay.

'Econometrics I' comprises chapters 1-8 of Wooldridge's “Introductory Econometrics. A Modern Approach”, in particular:

• Univariate regression model and the ordinary least squares estimator (OLS)
• Multivariate regression model (application, interpretation)
• Properties of the OLS estimator (classical assumptions, finite sample properties, asymptotic behaviour, Gauss-Markov theorem)
• Regression model inference (hypothesis testing, confidence intervals, model selection)
• Assumption failures (heteroskedasticity, serial correlation, multicollinearity)
• Functional forms (log-transformations, dummy variables, interaction terms)

Attendance is compulsory. Students are allowed to miss up to two units.

Midterm exam (40%), final exam (40%), assignments (20%). A positive combined exam mark (average of midterm and final) is required to be positive in the course.

The grading scheme is:

1. [90, 100]
2. [78, 90)
3. [65, 78)
4. [50, 65)
5. [0, 50)