Times series

Teacher(s)

von Sachs Rainer;

Language

English

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LSTAT2020	Logiciels et programmation statistique de base
LSTAT2120	Linear models

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.

Learning outcomes

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).

Content

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
6. Extensions to multivariate (bivariate) series

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 R-software). Some extensions for treating multivariate (bivariate) time series finalise the course.

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.

Online resources

https://moodle.uclouvain.be/course/view.php?id=1960

Bibliography

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

• Transparents sur moodle

Faculty or entity

LSBA