You need to activate javascript for this site.
Menu Conteúdo Rodapé
  1. Home
  2. Courses
  3. Economics
  4. Economic Data Analysis

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 - Quantitative sources of economic data
2 - Data and its statistical properties
3 - Methods of economic data analysis
4 – Panel data analysis: Fixed Effects and Random Effects in Static Models; Hausman Test; Wald Tests, Heteroscedasticity, Autocorrelation; Robust estimation.
Modeling and Estimation with practical applications using Stata
5 – Panel corrected standard errors (PCSE) estimation model;
6-Cointegration with Panel Data: Unit Root Tests 1st Generation and 2nd Generation. Cointegration Tests. Estimation of Models Pooled Mean Group (PMG), Mean Group (MG), Dynamic Fixed Effect (DFE), Dynamic Ordinary Least Squares (DOLS), Full Modified Ordinary Least Squares (FMOLS).
Main Bibliography REQUIRED READINGS
Asteriou, Dimitrios e Hall, Stephen G., Applied Econometrics, Palgrave Macmilan, 3rd Ed., 2015.
Baltagi, Badi H., Econometric Analysis of Panel Data, John Wiley & Sons, 5th edition, 2013.
RECOMMENDED READINGS
Armstrong, J. Scott, Illusions in regression analysis, in International Journal of Forecasting, Vol. 28, No. 3, July–September 2012, pp 689-694.
Greene, William H., Econometric Analysis, Global Edition Pearson Higher Education, 8th edition, 2020.
Wooldridge, Jeffrey, Econometric Analysis of Cross Section and Panel Data, The MIT Press, 2nd edition, 2010.
Teaching Methodologies and Assessment Criteria The evaluation criteria include the weighted result of two (2) teaching-learning moments, namely:
(1) 50% for the development, writing, presentation and discussion of an empirical work to be delivered on January 14, 2022; and 50% for Teaching-Learning attendance. Students who take the written exams (100%) of the final grade.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2022-01-28

The cookies used in this website do not collect personal information that helps to identify you. By continuing you agree to the cookie policy.