This course introduces modern econometrics, focusing on regression analysis. The goal is to learn enough theory and get enough practice to be able to read journal articles and for conducting basic empirical research.

A 2-hour weekly lecture for 10 weeks, ten hours of computer labs.

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■ Examination: two-hour end-of-course examination (75% of final grade for course)

■ Coursework: group assignment, consisting of an empirical (computer) exercise, indicative length 2000-2500 words (25% of final grade for course).

The aim of this course is to introduce modern econometrics. The goal is to learn enough theory and get enough practice to be able to read journal articles and for conducting basic empirical research.

The emphasis is on applying econometrics to real-world problems. However, a solid understanding of the reviewed inference procedures will require rudiments of probability theory and statistics, and the ability to prove few basic results.

The course focuses on regression analysis with cross-section data, under the familiar assumption of random sampling. This setting simplifies the exposition of the main results, requiring assumptions that are relatively straightforward yet realistic. The analysis of time series data is postponed to the last part of the course. This allows highlighting potential pitfalls that do not arise with cross-sectional data. Empirical exercises will be solved during computer lab sessions using an econometric software package.

By the end of the course, students should be able to:

1. Derive some of the fundamental numerical and statistical properties of the OLS estimator.

2. Critically interpret a regression output to answer a given research question.

3. Critically evaluate the assumptions of the classical linear regression model and the ways they can be modified and with what effects.

4. Translate an economic argument into a formal testable hypothesis within a multiple regression model and implement the appropriate testing procedure.

5. Select the appropriate econometric tools and apply them to implement an empirical analysis using an econometric/statistical software package.

6. Communicate effectively, using appropriate technical terms.

7. Work collaboratively in a group to produce a combined output, by liaising with other class members, allocating tasks and co-ordinating group meetings.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.