Advanced Econometrics II - Time Series Econometrics

lecon2601  2020-2021  Louvain-la-Neuve

Advanced Econometrics II - Time Series Econometrics
En raison de la crise du COVID-19, les informations ci-dessous sont susceptibles d’être modifiées, notamment celles qui concernent le mode d’enseignement (en présentiel, en distanciel ou sous un format comodal ou hybride).
5 crédits
30.0 h
Q2
Langue
d'enseignement
Anglais
Thèmes abordés
The course must cover the important and essential themes of the econometrics of time series analysis and their application in some fields of economics, like macroeconomics and finance. The basic concepts of stationarity and ergodicity are taught in the prerequisite course. The main themes for this course are those of linear time series models (autoregressive and moving average models), unit roots and cointegration. Both univariate and multivariate models must be taught. For non linear time series models, a selection of topics has to be done mainly among ARCH models, Makov-switching models, and state-space models. In all topics, the themes of model building, evaluation and prediction are included.
Acquis
d'apprentissage

A la fin de cette unité d’enseignement, l’étudiant est capable de :

1 The purpose is to train the students in the tools and models useful for the econometric analysis of economic time-series. Students will learn to understand in depth and apply correctly the techniques. The course prepares to research in the field of time-series analysis and its applications.
 

La contribution de cette UE au développement et à la maîtrise des compétences et acquis du (des) programme(s) est accessible à la fin de cette fiche, dans la partie « Programmes/formations proposant cette unité d’enseignement (UE) ».
Contenu
The course aims to find models that explain dynamical observations in economics. It considers the model-based method and attempts to infer model parameters by iteratively fitting observations with theoretical predictions from trial models. To this aim, it provides a necessary introduction to the basic theory of the following three types series: discrete-time Markov chain, continuous-time Markov chain, and continuous-time and continuous-state Markov processes.
The structure of the course is given as follows (subject to change)
1. Numerical methods
2. Stochastic numerical methods
3. Markov chains
4. Branching process
5. Continuous-time Markov chains
6. Birth and death processes
7. Continuous time Markov processes
8. Diffusion processes
9. Stochastic differential equations
10. Applications: competition, epidemic, population and spatial models
Méthodes d'enseignement

En raison de la crise du COVID-19, les informations de cette rubrique sont particulièrement susceptibles d’être modifiées.

Weekly lecture.
Modes d'évaluation
des acquis des étudiants

En raison de la crise du COVID-19, les informations de cette rubrique sont particulièrement susceptibles d’être modifiées.

Students are expected to complete a take-home final project by themselves. The project will consist of both analytical and empirical questions.
Ressources
en ligne
Bibliographie
William J. Stewart (2009), Probability, Markov Chains, Queues, and Simulation: The mathematical basis of performance modeling, Princeton University Press
Crispin Gardiner (2009), Stochastic Methods: A handbook for the natural and social sciences, 4th Edition , Springer 
Faculté ou entité
en charge


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

Intitulé du programme
Sigle
Crédits
Prérequis
Acquis
d'apprentissage
Master [60] en sciences économiques, orientation générale

Master [120] en sciences économiques, orientation générale

Master [120] en statistique, orientation générale

Master [120] en sciences économiques, orientation économétrie