Analysis of Economic Data II

Code:

2ECON36

Acronym:

AED2

Keywords
Classification	Keyword
CNAEF	Economics

Instance: 2020/2021 - 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	12	Bologna Syllabus	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
Markdown

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	10,00
Trabalho laboratorial	45,00
Trabalho prático ou de projeto	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
Trabalho laboratorial	15,00
Estudo autónomo	15,00
Total:	81,00

Eligibility for exams

The assessment will be based on:

• 2 assignments to be carried out during the semester;

• participation in class.

There is no evaluation by final exam.

 

Calculation formula of final grade

Distributed evaluation without final exam: two individual works (45% each); participation in class (10%). Course approval will not be given to students who do not obtain a minimum grade of 8 (out of 20) in each of the individual works.