Analysis of Economic Data I

Code:

2ECON35

Acronym:

AED1

Keywords
Classification	Keyword
CNAEF	Economics

Instance: 2021/2022 - 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	0	Bologna Syllabus	1	-	4,5	30	121,5

Teaching language

Portuguese

Objectives

The objective of the course is to provide students with effective empirical research skills. As a result of this module the student should become aware of best practices in accessing, assembling, and preparing data sets for research / economic analyses purposes.

Learning outcomes and competences

The student should be able to identify all major sources of economic data and to understand how to access the data. The student should also be able to collect data from structured and non-structured sources and to assemble complex datasets. Finally, students should learn how to efficiently organize data for economic analysis.

Working method

Presencial

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

The student should be familiar with statistical/econometric packages.

Program

1. Introduction: Economic data and metadata


2. Collecting structured economic data from official sources


3. Collecting non-structured economic data from other official sources


4. Accessing public data repositories


5. Managing confidential data


6. Data preparation for economic analysis

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

Complementary Bibliography

Long, J. S.; The workflow of data analysis using Stata. College Station, Stata Press, 2009
Edelman, B.; Using Internet Data for Economic Research, Journal of Economic Perspectives, 2012
Askitas, N., and Zimmermann, K. ; The internet as a data source for advancement in social sciences, International Journal of Manpower, 2015
Connelly, R., Playford, C. J., Gayle, V., and Dibben, C. ; The role of administrative data in the big data revolution in social science research, Social Science Research, 2016

Teaching methods and learning activities

There will be a small amount of theoretical exposition, but most classes will be applied and oriented towards the solution of practical and real cases. Working group dynamics will be stimulated. 

Software

Stata

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	10,00
Trabalho prático ou de projeto	40,00
Teste	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	31,50
Frequência das aulas	30,00
Trabalho escrito	60,00
Total:	121,50

Eligibility for exams

The assessment will be based on:

• 1 exam


• 1 group assignment;


• participation in classes.

There is no evaluation by final exam.

Calculation formula of final grade

Distributed evaluation without final exam: team work (40%); 
exam (50%); participation in classes (10%). Course approval will not be given to students who do not obtain a minimum grade of 7 (out of 20) in the exam.