Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
2 credits
15.0 h
Language
English
Main themes
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.
Content
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
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
Teaching methods
Weekly lecture.
Evaluation methods
Students are expected to complete a take-home final project by themselves. The project will consist of both analytical and empirical questions.
Online resources
Moodle UCL ( > https://moodleucl.uclouvain.be/).
Bibliography
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
Crispin Gardiner (2009), Stochastic Methods: A handbook for the natural and social sciences, 4th Edition , Springer
Faculty or entity
ECON
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Economics: Econometrics
