| Code |
18203
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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
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Entry requirements |
NA
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Learning outcomes |
- Study advanced econometric techniques to analyze complex data related to Circular Economy and Energy.
-Applying econometric methods to evaluate the impact of Circular Economy and Energy policies.
-Apply econometric methods to analyze energy markets.
-To enable students to use circular and energy forecasting techniques, whether in the short, medium, long or very long term.
- Ability to formulate and interpret econometric models, methods and techniques
-To enable students to apply the different methods using software (R)
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Syllabus |
1. Descriptive analysis of time series: timeline 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. Univariate models: Box-Jenkins methodology and ARIMA models
4. Multivariate models VAR, VECM, Threshold-AR, Markov-Switching
5. Modeling conditional heteroscedasticity: univariate and multivariate cases.
6. Forecasting time series with autoregressive neural networks.
7. Difference-in-differences model
8. Discontinuous Regression Model
9. Propensity Score Matching
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Main Bibliography |
Roth, Jonathan, et al (2022); What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature, Cornell University
Hrishikesh D Vinod (2022), Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software (Second Edition), World Scientific Pub Co Inc
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
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Teaching Methodologies and Assessment Criteria |
Teaching is based on theoretical-practical classes, in which, after theoretical content has been transmitted, students solve practical exercises in order to better assimilate theoretical concepts and develop their ability to apply models. When solving exercises, priority is given to using real data with the support of appropriate econometric software.
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Language |
Portuguese. Tutorial support is available in English.
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