| Code |
12431
|
| Year |
1
|
| Semester |
S2
|
| ECTS Credits |
6
|
| Workload |
OT(15H)/TP(30H)
|
| Scientific area |
Economics
|
|
Entry requirements |
The basics of statistics.
|
|
Mode of delivery |
Face-to-face
|
|
Work placements |
Not applicable
|
|
Learning outcomes |
-Learn the importance of economic forecasting
-Learn some of the most important forecasting techniques in economics, whether short, medium, long, or very long term
-Learn how to deal with seasonality in economic forecasting using several methodologies
-Learn to perform economic forecasts in the short, medium and long term and even very long term.
Learn to perform simultaneous forecasts of several variables using VAR and VAR/ECM (VECM) models and neural networks.
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|
Syllabus |
- Introduction to Forecasting and Model Selection in Economics
- Time Series Decomposition and Forecasting by the Classical Method: Trend, Seasonal, Cyclical and Irregular Effects
- Forecasting with Simple and Double Moving Averages and Simple and Double Exponential Smoothing
- Forecasting with Simple and Multiple Regression Models
- Forecasting with Advanced Models: Box-Jenkins Methodology and ARIMA models
- Prediction of Series with Additive and Multiplicative Seasonality
- Multiple Variables Forecasting with VAR and VAR/ECM Models
- Series forecasting with neural networks.
|
|
Main Bibliography |
Elliott, G., & Timmermann, A. (2016). Economic Forecasting. Princeton University Press.
Fuleky, P. (2020). Macroeconomic Forecasting in the Era of Big Data. Springer
Ghysels, E., & Marcelino, M. (2018). Applied Economic Forecasting using Time Series Methods. Oxford University Press
Mills, T. C. (2019). Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting. Academic Press.
Powers, D. (2019). On Trend: The Business of Forecasting the Future. University of Illinois Press.
Agung, I. G. N. (2019). Advanced Time Series Data Analysis: Forecasting Using Eviews. Wiley
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Language |
Portuguese. Tutorial support is available in English.
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