Analysis of Economic Data II

Code:

2ME20

Acronym:

AED II

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2022/2023 - 1S

Active?	Yes
Course/CS Responsible:	Master in Economics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
ME	36	Official Syllabus after 2021-2022	1	-	3	21	81

Teaching language

Portuguese

Objectives

This course is a follow-up to “Analysis of Economic Data I”. The student should understand the importance of creating reproducible research and empirical work in economics and learn good practices to that end.

Learning outcomes and competences

As a result of the course the student should be able to implement best practices in empirical work, namely with respect to the process of making sure that the research is replicable.  

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

This course requires some knowledge acquired in the UC “Analysis of Economic Data I”.

Program

 

1. The empirical information production process in economics
2. Reproducible research in economics and econometrics
3. Effective implementation of empirical work: Proper coding, version control and good workflow
4. Automation of data analysis
5. Replication of published economic empirical research: case studies

Mandatory literature

Corti, L., Van den Eynden, V., Bishop, L., and Woollard, M.; Managing and Sharing Research Data: A Guide to Good Practice, Sage Publications, 2020
Orozco, V., Bontemps, C., Maigné, E., Piguet, V., Hofstetter A., Lacroix A., Levert, F., and Rousselle, J.M; How to Make a Pie: Reproducible Research for Empirical Economics & Econometrics, TSE Working Paper, n.18-933, 2018

Complementary Bibliography

Long, J. S.; The workflow of data analysis using Stata. , College Station, TX: Stata Press., 2009
Gentzkow, M., and Shapiro, J.M. ; Code and Data for the Social Sciences: A Practitioner's Guide, 2014

Teaching methods and learning activities

 

There will be a small amount of theoretical exposition, but most classes will be applied. In computer sessions, students must:

(i) prepare reports on empirical work using Markdown language;

(ii) replicate published economic studies.

Software

Stata
Jupyter Notebook

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	10,00
Trabalho prático ou de projeto	45,00
Teste	45,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Frequência das aulas	21,00
Trabalho de investigação	30,00
Estudo autónomo	30,00
Total:	81,00

Eligibility for exams

The assessment will be based on:

• 1 exam

• 1 group assignment;

• participation in class.

There is no evaluation by final exam.

 

Calculation formula of final grade

Distributed evaluation without final exam: team work (45%); 
exam (45%); participation in classes (10%). Students who obtain a final classification equal to or greater than 9.5 values ​​and who have not obtained a classification lower than 6.5 values ​​in the exam are approved.