Advanced Computer Skills
A.Y. 2022/2023
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: First trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
First 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, globals, matrices and loops.
Topic 7 Produce graphs with Stata
Topic 8 Regressions and hypothesis testing: correlation, ttest, chi2 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, globals, matrices and loops.
Topic 7 Produce graphs with Stata
Topic 8 Regressions and hypothesis testing: correlation, ttest, chi2 test, linear regressions, instrumental variable regressions, probit and logit.
Topic 9 Producing descriptives and regression tables in Stata.
Prerequisites for admission
The course participants should already have attended some introductory statistics and be familiar with methods of linear regression analysis.
Teaching methods
The lectures of the course will take place only in presence and students will have to bring their own laptops.
Language of the course: English.
Before each lecture, lecture notes and required files are uploaded on Ariel.
Students need to submit their class-assignments using the dedicated Teams.
Language of the course: English.
Before each lecture, lecture notes and required files are uploaded on Ariel.
Students need to submit their class-assignments using the dedicated Teams.
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
- 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 incentivize students to revise lectures, the evaluation for the course will in part consist of some assignments during classes and of one homework.
Attending students:
Assignments - 25% of final grade: at the end of each lesson/topic there will be some Stata assignments. Students will be required to replicate a piece of analysis.
Homework - 25% of final grade: there will be one homework to be done in small groups (1-2-3 students) to deliver in one week's time.
Exam - 50% of final grade: an assignment to be performed in class during the last lecture.
Non-attending students
Written exam: the exam will consist of two parts:
multiple-choice questions on data management, Stata commands.
an assignment to be performed
Attending students:
Assignments - 25% of final grade: at the end of each lesson/topic there will be some Stata assignments. Students will be required to replicate a piece of analysis.
Homework - 25% of final grade: there will be one homework to be done in small groups (1-2-3 students) to deliver in one week's time.
Exam - 50% of final grade: an assignment to be performed in class during the last lecture.
Non-attending students
Written exam: the exam will consist of two parts:
multiple-choice questions on data management, Stata commands.
an assignment to be performed
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Professor:
Mazzarella Gianluca
Educational website(s)