Times series

lstat2170  2020-2021  Louvain-la-Neuve

Times series
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
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).
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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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

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