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Learning Objectives

LEARNING OUTCOMES: The aim of this course is to acquaint students with the basics of R, MATLAB, Stata and Python and their usage in applied economics.

KNOWLEDGE AND UNDERSTANDING: the final goal is to gain knowledge of the analytical tools to understand the most common micro and macro-econometric models, even in a research context.

APPLYING KNOWLEDGE AND UNDERSTANDING: develop the ability to deal with the empirical analysis of micro and macro models in a systematic way.

MAKING JUDGEMENTS: acquire the computational and methodological tools to analyze the choices of the national and european policy makers.

COMMUNICATION SKILLS: students must be able to deliver the emprical results, in a rigorous way, to an (expert or non-expert) audience.

LEARNING SKILLS: students can undertake the in-depth study of the considered, or other, softwares.



Students are supposed to be attending, or have attended, the course of Statistics and Econometrics.


Stata: mechanics (do files, data and datasets), programming (macros, scalar, matrice, branching and looping), descriptives (graphs and tables), estimation and interpretation of the linear regression model.

Python: python essentials, functions and objects, data structures, data visualization, some applications of python on economic models.


All the material (slides, data files, scripts) will be posted on the course webpage.


Suggested readings
- Bourke (2018). "Computer Science I", available at
- Microeconometrics Using Stata, A. C. Cameron and P. K. Trivedi, Stata press
- Python for Everybody, Exploring Data using Python 3, by Charles Severance
- Stata documentation (any version)
- Christopher F. Baum (2016), An Introduction to Stata Programming, Second Edition, Stata Press
- An Introduction to Modern Econometrics using Stata, C.F. Baum, 2006
- Statistics with Stata, by L.C. Hamilton, 2006
- Mastering Metrics, by J. Angrist and S. Pischke, 2015
- An Introduction to Stata Programming, Christopher F. Baum, 2014

Exam Rules

The exam consists of a practical test, in presence, on the three parts of the course: R/Matlab, Stata and Python. It is necessary to obtain a positive evaluation for all the three parts to pass the exam. The result of the exam is a single mark (pass or fail), and it doesn't give any exemption on single parts of the course in any case.
Students who withdraw or fail an exam may take the exam again in the same exam session.
During the period of the lessons, some intermediate tests will be held to verify the students' achievements. A positive evaluation in one or more parts will guarantee an exemption for those parts from the final exam. The exemption will remain valid for the entire academic year.
The rules to obtain a positive evaluation are different for each part and are specified in the "teaching material" section.
Students that don't obtain a positive evaluation in all the three parts will have to sit the final exam for all the parts in which they failed.
Students must book the exam through Delphi and be present on the exam date. The same also hold for students that obtain a positive evaluation to all the intermediate tests.



R and MATLAB: data import and export, plotting commands, descriptive statistics, functions for random variables, the likelihood approach, the regression model, time series models.


Suggested readings
- Bourke (2018). "Computer Science I", available at
- Davies (2016). "The book of R". No starch press
- Cho, Martinez (2014). Statistics in MATLAB: A Primer. Chapman and Hall/CRC

Teaching methods

Not compulsory, but strongly recommended.