Advanced Computer Skills
A.Y. 2024/2025
Learning objectives
The course objectives are to:
·Provide students with the understanding on the main basic tools of Stata programme used for the analysis of economic data.
·Provide students with some knowledge on the main Stata commands and functions, also through some practical applications, examples and results of the academic research.
·Offer opportunities to replicate the analysis and the empirical results of some seminal scientific articles.
·Give students the means to perform independent empirical analysis for future work including the final master dissertation, but also for other courses delivered in the EPS Master programme(Global firms and markets, Comparative Politics e Empirical Methods for Economics and Policy Evaluation)
·Provide students with the understanding on the main basic tools of Stata programme used for the analysis of economic data.
·Provide students with some knowledge on the main Stata commands and functions, also through some practical applications, examples and results of the academic research.
·Offer opportunities to replicate the analysis and the empirical results of some seminal scientific articles.
·Give students the means to perform independent empirical analysis for future work including the final master dissertation, but also for other courses delivered in the EPS Master programme(Global firms and markets, Comparative Politics e Empirical Methods for Economics and Policy Evaluation)
Expected learning outcomes
Students will acquire a set of skills that will be useful for future empirical work, both within and outside academia:
·Data management: structure and use;
·Creation of a workflow e use of do-files (automation of tasks where possible, management of largedataset, management of directories, etc.);
·Linear regression model estimation;
·Creation of descriptive statistics with tables and graphs;
·Creation of regression output tables;
·Understanding and interpretation of empirical results from scientific articles.
·Data management: structure and use;
·Creation of a workflow e use of do-files (automation of tasks where possible, management of largedataset, management of directories, etc.);
·Linear regression model estimation;
·Creation of descriptive statistics with tables and graphs;
·Creation of regression output tables;
·Understanding and interpretation of empirical results from scientific articles.
Lesson period: Second trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Professor(s)
Reception:
To be agreed by scheduling an appointment
Room 37 (3rd floor) or Microsoft Teams