Times series

Teacher(s)

von Sachs Rainer;

Language

French

Prerequisites

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

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.

Aims

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”.

Content

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 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).

Other information

Prerequisites A general knowledge of basic statistical concepts (on the level of a first introductory course in statistics) is necessary. Evaluation The examination will be oral. An applied data analysis project has to be prepared on the computer. Teaching material Course notes, von Sachs, R. and S. Van Bellegem, Script. References : Brockwell, P., Davis, R. : Introduction to Time Series and Forecasting. 1996, Springer, New York Brockwell, P., Davis, R. : Times Series : Theory and Methods. 1991, Springer, New York Gourieroux, Ch. : Modèles ARCH et applications financières. 1992, Economica, Paris

Faculty or entity

LSBA