Advanced computer skills

A.Y. 2020/2021
3
Max ECTS
20
Overall hours
SSD
INF/01
Language
English
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)
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.
Course syllabus and organization

Single session

Responsible
Lesson period
First trimester
The lectures of the course will take place in presence and students will have to bring their own laptop. Students who cannot access the University can follow and participate to classes in streaming via Zoom or Teams. There will also be a tutor supporting students during lectures.
· Live classes will be recorded, and lecture slides will be shared, to facilitate students' independent learning. All the course material will be saved on the Ariel course page.
· In order to incentivize students to revise lectures, the evaluation for the course will in part consist of short quizzes on the material of the previous lecture and will reward students' participation to the live lectures.
· In person office hours, for small groups, will be held weekly to ensure also in person interaction with the instructor for students who wish or can attend. Similar opportunities for online office hours will be given to students not able to access the University.
Course syllabus
Students will find the detailed course calendar and the lecture slides on the course Ariel website, containing all the information on required readings for each lecture.
The course will cover the following topics:
Topic 1 - Introduction to Stata, the help tool, log and do files, file formats.
Topic 2 - Data management: file creation, file import and export. Importing data from excel and other non-dta formats. Managing data formats, commands for data variable management, commands for exploratory analysis.
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.
Topic 4 - Working with data I: Deal with different types of variables (categorical, continuous, dummies). Merging files. Survey data vs administrative data. Exporting descriptive tables. Commands like tabulate, summarize, describe, list, outreg, estout.
Topic 5 - Working with data II: Creating and exporting different types of graphs: histogram, twoway scatter, twoway line, twoway connected. Saving, exporting, modifying graphs. Time formats variables.
Topic 6 - Regressions and hypothesis testing: Commands correlate and regress. Main tools for hypothesis testing and confidence intervals checking (table, ttest, and other comparison methods). Regression with robust and clustered standard errors.
Topic 7 - Instrumental variable regressions: How to run an instrumental variable regression analysis.
Topic 8 - Examples from academic articles on the role of institutions and the impact of microcredit.

This course will 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).
Prerequisites for admission
The course participants should already have attended some introductory statistics and be familiar with methods of linear regression analysis.
Teaching methods
Lectures: lectures will consist of presentations over slides, polls and quizzes. The lectures will take place in presence at the University and students will have to bring their own laptop. Lectures will be on streaming for students who are not able to access the University. Lectures will be recorded.
Teaching Resources
The main material for the course are the lecture slides, which will be regularly uploaded on the course Ariel website.
Students will also find the detailed course calendar and the lecture slides on the course Ariel website, with all the information on required readings for each lecture.
In general, there is no required textbook for the course. The main study material are slides and academic papers. Some useful books are:
· 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
In addition, articles from academic journals will be assigned as required or recommended reading and reviewed in the lectures and classes.
Assessment methods and Criteria
Evaluation criteria
A combination of assignment methods is planned, in order to evaluate and reward:
· Knowledge of the learning material;
· Participation and engagement;
· The ability to use some basic Stata commands, create do-files;
· Data analysis: creation of graphs, descriptive statistics and regression tables;
· The ability to set up a simple empirical analysis;
· The ability to reproduce simple tables from scientific articles.
Attending students
· Participation (20% of the final grade): participation will be monitored during the lectures, in terms of participation to class discussions.
· Assignments: there will be Stata assignment (30% of final grade): students will be required to replicate a piece of analysis (e.g., a regression table or a graph) from an academic paper discussed during the lectures and the Stata classes.
· Exam (50%): a short assignment to be performed in class during the last lecture.
Non-attending students
Written exam: the exam will include multiple-choice questions on data management, Stata commands and functions and some practical examples on academic papers discussed in the slides.
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Professor: Rosso Anna Cecilia
Educational website(s)
Professor(s)
Reception:
Tuesdays, 10.30-12.30
Room 11, 2° floor, DEMM