Descriptif
The objective of this course is to present the fundamental concepts of time series analysis. Completion of this
course will enable students to move on to more advanced courses on time series modeling. The lectures will present the main concepts of linear time series and the methods to fit a model on the data.
Brockwell, P.J. and R.A. Davis (1991) Time Series: Theory and Methods. 2nd Edition, Springer
Brockwell, P.J. and R.A. Davis (2002) Introduction to Time Series and Forecasting, Springer
Gouriéroux, C. and A. Monfort (1997) Time Series and Dynamic Models, Cambridge University Press,
Cambridge
Hamilton, J. D. (1994) Time Series Analysis, Princeton University Press
Objectifs pédagogiques
At the end of the course, the student should be able to
Compute and interpret a correlogram, discuss the concepts of stationarity and white noise
Derive the probabilistic and statistical properties of linear time series models
Choose an appropriate ARIMA model for a given set of data and use it for forecasting
Handle multivariate time series and discuss the notions of cointegration and causality
effectifs minimal / maximal:
/52Diplôme(s) concerné(s)
- M1 MiE - Master en Economie
- Programmes d'échange internationaux
- Titre d’Ingénieur diplômé de l’École polytechnique
Parcours de rattachement
Pour les étudiants du diplôme M1 MiE - Master en Economie
Some basics in algebra (complex numbers, roots of polynomials), probability and statistics (estimation and tests)
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme Titre d’Ingénieur diplômé de l’École polytechnique
Vos modalités d'acquisition :
final written exam
Le rattrapage est autorisé (Note de rattrapage conservée)- Crédits ECTS acquis : 5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme M1 MiE - Master en Economie
Le rattrapage est autorisé (Note de rattrapage conservée)- Crédits ECTS acquis : 3 ECTS
Pour les étudiants du diplôme Programmes d'échange internationaux
Vos modalités d'acquisition :
written closed book final exam
L'UE est acquise si Note finale >= 10Programme détaillé
Examples of time series. Aims of Time Series analysis.
1. Generalities on univariate second-order stationary processes - Autocovariances, partial autocorrelations
- Innovations - Wold theorem - Asymptotic properties of empirical moments
2. AR, MA, ARMA, SARIMA processes - Canonical representation - Identification, estimation, tests and
forecasting - Model building
3. Nonstationary models, Unit root tests
4. Stationary vector processes - Multivariate AR models - Statistical Inference - Causality tests, impulseresponse
analysis. Cointegration