| Code |
15437
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
TP(30H)
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| Scientific area |
Economics and Management
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Entry requirements |
not applicable
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Learning outcomes |
Aims:
- To develop the skills in, and knowledge of, econometrics necessary for theoretical and empirical work based on time-series and forecasting techniques;
- To learn how to conduct empirical studies in economics and financial to related fields using parametric and non-parametric econometric techniques;
- To learn the importance of forecasting economic magnitudes or variables;
- To learn some of the more important techniques of forecasting economic and financial variables, either in the short,medium, long and very long terms.
Competences:
- Data analysis and manipulation;
- Select and apply appropriate techniques to solve econometric problems;
- Team work, written and oral communication;
- Computing skills and knowledge of econometric software.
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Syllabus |
1. Descriptive analysis of time series: chronogram and identification of the components of a time series (trend, cycle, seasonality, residual component).
2. Smoothing methods: moving averages, single, double and triple exponential smoothing.
3. Multivariate models: VAR, VECM, Threshold-AR, Markov-Switching
5. Forecasting time series with autoregressive neural networks
6. Difference-in-differences modelling
7. Propensity Score Matching
8. Machine learning topics applied to econometrics
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Main Bibliography |
M. Hashem Pesaran (2015), Time Series and Panel Data Econometrics, Oxford
Verbeek, M. (2018), A Guide to Modern Econometrics, 5th Ed., Wiley.
Gertler, Paul J., et al.(2016) Impact Evaluation in Practice, World Bank Group
Charles S. Reichart (2019) Quasi-Experimentation: A Guide to Design and Analysis (Methodology in the Social Sciences), The Guilford Press
Roth, Jonathan, et al (2022); What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature, Cornell University
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Teaching Methodologies and Assessment Criteria |
Teaching is based on theoretical and practical classes with a small number of students, in which, after the theoretical content has been presented, students solve practical exercises that help them understand the concepts and put their knowledge into practice. When solving exercises, priority is given to the use of real data with the support of appropriate econometric software.
The teaching-learning assessment consists of an individual practical assignment on 13 May (60%) and an individual written test on 22 May (40%).
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Language |
Portuguese. Tutorial support is available in English.
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