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Learning Objectives

Learning outcomes: The aim of this course is to introduce the students to state-of-the-art techniques for the estimation of causal effects, for both cross-sectional and longitudinal data.

Knowledge and understanding: On successful completion of the course, students will be able to understand causal inference tools. Specifically, we will study the potential outcome model, discuss the main assumptions required for the identification of average causal effects, and focus on the main estimation approaches. Emphasis will be placed on identification and consistent estimation.

Applying knowledge and understanding: On successful completion of the course, students will be able to apply the discussed techniques using the Stata statistical software.

Making judgements: On successful completion of the course, students will be able to orient themselves between different techniques and choose the best for the case at hand.

Communication skills: Students will be able to present and effectively communicate the results of their own elaborations and analyses.



Students should have completed Mathematics (8011190), Statistics (8010848) and Econometrics (8011571). A good knowledge of Stata is required.


- A short introduction to Monte Carlo simulation

- Linear panel data models
Static models and main assumptions
Fixed- and random-effects estimation
Homogeneity and specification tests
Dynamic models

- Models for limited dependent variables
A short introduction to bootstrap, jackknife and delta methods for variance estimation
Binary outcomes
Multinomial outcomes
Count outcomes
Truncation and sample selection


The main reference is Wooldridge J.M., (2010), Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Cambridge (MA).

For some topics, Wooldridge (2010) will be complemented by selected articles which will be made available on the material section of the course website and by some chapters from:

Greene W.H., Econometric Analysis, 8th ed., Pearson.


For further study in econometrics, I suggest Peracchi (2001), Davidson and MacKinnon (2004) and references therein:

Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
Davidson R. and MacKinnon J.G., Econometric Theory and Methods, New York, Oxford University Press, 2004.

Teaching notes and suggestions for further reading will be provided in class.

Teaching methods

The course consists of eighteen theoretical lectures, with applications based on simulated and real micro-data.

Exam Rules

The final exam is a one and a half hours written test consisting of three exercises. Some exercises will report Stata’s output and will require their interpretation. To pass the exam, the student must get a mark of 18 in at least two exercises. The final mark will be computed as the average of the three exercises. For each exercise, the mark can range from 0 to 33, so students can obtain a final mark of 30 even without getting a mark of 30 to all exercises. The exam is aimed at assessing whether the student has acquired a solid knowledge of the topics covered during the course, both from a theoretical and a practical point of view.

Students must book for the final exam on https://delphi.uniroma2.it. Students who fail or withdraw from the exam may take it again in the same exam session.