# Applied Econometrics : Time Series

lecon2031  2019-2020  Louvain-la-Neuve

Applied Econometrics : Time Series
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 12.0 h
Q1
Teacher(s)
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.

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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
Teaching methods
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
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
Bibliography
Livre de référence (Reference book):
Introductory Time Series with R (2009), Paul S.P. Cowpertwait, Andrew V. Metcalfe.
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

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Mathematical Engineering

Master [120] in Statistic: General

Master [120] in Agriculture and Bio-industries

Master [120] in Economics: General