Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 12.0 h
Q1
Teacher(s)
Panin Amma;
Language
English
Main themes
Time series analysis requires to understand the notions of stationarity and non-stationarity, which will be pre-sented in an intuitive and detailed way by the use of examples of macroeconomic and financial time series. Then, econometric models adapted to model such series will be explained and applied. The theme of prediction is obviously very important for time series and will be covered for each type of model. Although the course is focused on the univariate approach, an introduction to multivariate aspects is foreseen. Inference methods (like ordinary least squares and maximum likelihood) are taught or reminded in the context of the models that require them.
Aims
At the end of this learning unit, the student is able to : | |
| 1 | The objective is to train students to use econometric methods for modelling and predicting economic and finan-cial time series. The emphasis is put on applications in macroeconomics and finance, and to the extent necessary for that, on understanding the methods and models. |
Content
(subject to change)
1. Time Series Data and Programming
2. Stationarity
3. Moving Average Model (MA)
4. Auto-Regressive Model (AR)
5. ARMA Modeling
6. Non-stationarity and Integrated process
7. Filters and Seasonality
8. System Identification
9. Vector AR
10. VAR Modeling
11. Kalman Filter
1. Time Series Data and Programming
2. Stationarity
3. Moving Average Model (MA)
4. Auto-Regressive Model (AR)
5. ARMA Modeling
6. Non-stationarity and Integrated process
7. Filters and Seasonality
8. System Identification
9. Vector AR
10. VAR Modeling
11. Kalman Filter
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
The course includes lectures by the lecturer and tutorials supervised by an assistant.The teacher explains the theory and some implementations. The methods are each illustrated by examples of application in various fields of the economy.
During the practical work sessions, students learn to apply the methods seen during the course on real data. This learning is done with the software R.
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
There are two parts to the exam: (1) a writing exam (14 points out of 20), and (2) a practical part with R (6 points out of 20). The second part consists of two home assignments.
Online resources
See Moodle UCL ( > https://moodleucl.uclouvain.be/).
Bibliography
Livre de référence (Reference book):
Applied Econometric Time Series, 4th Edition (2014), Walter Enders (Older editions are fine)
Autres livres de référence (Other reference books)
Time Series Analysis and Its Applications with R Examples (2011), 3rd Edition, Robert H. Shumway, David S. Stoffer
Time Series Analysis: Forecasting and Control (2015), 5th Edition, George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung
Applied Econometric Time Series, 4th Edition (2014), Walter Enders (Older editions are fine)
Autres livres de référence (Other reference books)
Time Series Analysis and Its Applications with R Examples (2011), 3rd Edition, Robert H. Shumway, David S. Stoffer
Time Series Analysis: Forecasting and Control (2015), 5th Edition, George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung
Faculty or entity
ECON
