| Code |
16299
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
|
| Workload |
TP(30H)
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| Scientific area |
Economics
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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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Language |
Portuguese. Tutorial support is available in English.
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