Times series

lstat2170  2019-2020  Louvain-la-Neuve

Times 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
22.5 h + 7.5 h
von Sachs Rainer;
Main themes
The principal subjects of this course on an introduction into time series analysis will include the modelling, estimation and prediction of two types of processes - linear processes and heteroscedastic models of non-linear processes. We follow basically a parametric approach - the student will learn how to quantify statistical uncertainty while estimating the model parameters for the problem of forecasting future values of the observedseries.

At the end of this learning unit, the student is able to :

1 The aim of this course is to give a good comprehension of the theory and application of stochastic time series modelling, with a view towards prediction (forecasting).

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”.
1. Modelling time series data: an introduction
2. Linear processes - simple parametric models (ARMA)
3. Estimation and prediction of ARMA models
4. Box-Jenkins analysis - (S)ARIMA models
5. Non-linear processes - heteroscedastic (G)ARCH models - applications to modelling financial data
Teaching methods
Basic models of linear time series will be treated in the first part. The data analysis, i.e. estimation of the model parameters for forecasting, will be based predominantly on Box-Jenkins methods. In the second part of the course some elements of modelling financial data with the more recently developed ARCH and GARCH models will be given and included into the practical part of the course (done with the S-Plus software).
Evaluation methods
The examination will be oral. An applied data analysis project has to be prepared on the computer.
Other information
Prerequisites A general knowledge of basic statistical concepts (on the level of a first introductory course in statistics) is necessary.
Brockwell, P. and R. Davis (1996), Introduction to Time Series and Forecasting. Springer, New York
Brockwell, P and R. Davis (1991), Time Series, Theory and Methods. Springer, New York
Gourieroux, Ch. (1992), Modèles ARCH et applications financières. Economica, Paris
Teaching materials
  • syllabus et transparents sur moodle
Faculty or entity

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

Title of the programme
Master [120] in Biomedical Engineering

Master [120] in Mathematical Engineering

Master [120] in Data Science : Statistic

Master [120] in Statistic: Biostatistics

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Economics: General

Master [120] in Actuarial Science

Master [120] in Statistic: General