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Economic Data Analysis

Code 12416
Year 1
Semester S1
ECTS Credits 6
Workload OT(15H)/TP(30H)
Scientific area Economics
Entry requirements N.A.
Mode of delivery Face-to-face
Work placements Not applicable
Learning outcomes The aim of module is designed to introduce students to: (1) the state of the art of economic analysis of data; (2) applied
economic data analysis; and (3) the use of econometric software (EViews, STATA and RATS).
The learning outcomes of de module are: (1) to provide the understanding of economic analysis of data; (2) enabling
students to develop a solid econometric analysis; and (3) qualifying students to economic and financial decisionmaking.
Syllabus 1 - Sources of quantitative economic data
2 - Data and its statistical properties
3 - Methods and techniques
4 - Presentation of results
5 - Discussion of the results
6 - Advanced topics in econometrics
7 - Practical aspects of the use of econometric software
8 - Replication of seminal research
9 – Principles those econometricians must be present
10 - Ethical issues in applied research
Asteriou, Dimitrios e Hall, Stephen G., Applied Econometrics, Palgrave Macmilan, 2nd Ed., 2011.
Baltagi, Badi H., Econometric Analysis of Panel Data, John Wiley & Sons, 4th edition, 2008.
Armstrong, J. Scott, Illusions in regression analysis, in International Journal of Forecasting, Vol. 28, No. 3, July–
September 2012, pp 689-694.
Becketti, Sean, Introduction to Time Series Using Stata, Stata Press, Texas, 2013.
Brooks, Chris, Introductory Econometrics for Finance, Cambridge University Press, 2nd Ed., 2008.
Enders, Walter, Applied Econometric Time Series, John Wiley & Sons, 3rd edition, 2009.
Greene, William H., Econometric Analysis, Pearson Higher Education, 7th edition, 2011.
Juselius, Katarina, The Cointegrated VAR Model: Methodology and Applications, Oxford University Press, 2006.
Wooldridge, Jeffrey, Econometric Analysis of Cross Section and Panel Data, The MIT Press, 2nd edition, 2010.
Teaching Methodologies and Assessment Criteria The topics/themes of Economic Data Analysis are set by the teacher during lectures. The lecturing in practical classes
involves the resolution of exercises, the discussion of seminal articles, the collection of data, and the econometric
handling of the models.
The grading results from the weighting of three moments of learning as follows: (1) class participation 10%; (2)
development, writing, presentation and discussion of empirical work 40%; and (3) final examinations 50%.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2016-06-02

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