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
Single session
Responsible
Lesson period
Second trimester
Course syllabus
The course will cover the following topics:
Topic 1 - Introduction to Stata, installation, environment, the help tool, log and do files, file formats.
Topic 2 - Basic operation with dataset: Load a dataset; browse/edit; count; describe; type of variables: strings, numeric, categorical; summarize; tabulate; generate new variables; eliminate existing variables; label var, label values; conditions; missing values; save.
Topic 3 Do-file and workflow creation for the empirical analysis. From data management to descriptive analysis, how to organize work, create descriptive tables and graphs. Variables creation and management of missing values; importing dataset not in Stata format.
Topic 4 Operations across rows (mean min, max, median, sd, percentiles, etc).
Topic 5 Rearrange datasets (reshape long and wide) and combine different datasets.
Topic 6 Local and globals macros, matrices and loops.
Topic 7 Produce graphs with Stata.
Topic 8 Regressions and hypothesis testing: correlation, t-test, chi-squared test, linear regressions, instrumental variable regressions, probit and logit.
Topic 9 Producing descriptives and regression tables in Stata.
Topic 1 - Introduction to Stata, installation, environment, the help tool, log and do files, file formats.
Topic 2 - Basic operation with dataset: Load a dataset; browse/edit; count; describe; type of variables: strings, numeric, categorical; summarize; tabulate; generate new variables; eliminate existing variables; label var, label values; conditions; missing values; save.
Topic 3 Do-file and workflow creation for the empirical analysis. From data management to descriptive analysis, how to organize work, create descriptive tables and graphs. Variables creation and management of missing values; importing dataset not in Stata format.
Topic 4 Operations across rows (mean min, max, median, sd, percentiles, etc).
Topic 5 Rearrange datasets (reshape long and wide) and combine different datasets.
Topic 6 Local and globals macros, matrices and loops.
Topic 7 Produce graphs with Stata.
Topic 8 Regressions and hypothesis testing: correlation, t-test, chi-squared test, linear regressions, instrumental variable regressions, probit and logit.
Topic 9 Producing descriptives and regression tables in Stata.
Prerequisites for admission
Participants should already have attended some introductory statistics course and be familiar with methods for linear regression analysis.
Teaching methods
The lectures of the course will take place only in presence.
Language of the course: English.
Before each lecture, lecture notes and required files are uploaded on Microsoft Teams.
Language of the course: English.
Before each lecture, lecture notes and required files are uploaded on Microsoft Teams.
Teaching Resources
Materials will be regularly uploaded on Microsoft Teams for each lecture.
There is no required specific textbook for the course, however some useful books are the following ones:
- Cameron, C. and Trivedi, P. K. (2010) "Microeconometrics Using Stata, Revised Edition". Stata Press
- Gentzkow, M. and Shapiro, J. M. "Code and Data for the Social Sciences: A Practitioner's Guide". To be found here: https://web.stanford.edu/~gentzkow/research/CodeAndData.xhtml
There is no required specific textbook for the course, however some useful books are the following ones:
- Cameron, C. and Trivedi, P. K. (2010) "Microeconometrics Using Stata, Revised Edition". Stata Press
- Gentzkow, M. and Shapiro, J. M. "Code and Data for the Social Sciences: A Practitioner's Guide". To be found here: https://web.stanford.edu/~gentzkow/research/CodeAndData.xhtml
Assessment methods and Criteria
In order to push students to attend lectures, during the course students will have to carry out some exercises in class. In addition, students will be asked to do homeworks. The latter points replace the written exam.
For non-attending students, the assessment will consist of a written or computer based exam containing:
- multiple choice questions about data management and some Stata commands
- writing of Stata code
For non-attending students, the assessment will consist of a written or computer based exam containing:
- multiple choice questions about data management and some Stata commands
- writing of Stata code
Professor(s)
Reception:
To be agreed by scheduling an appointment
Room 37 (3rd floor) or Microsoft Teams