Course syllabus
Course PM
This page contains the program of the course: lectures, exercise sessions and projects. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.
Program
The schedule of the course is in TimeEdit.
The following program is preliminary and may be updated as the course progresses. Check the course page regularly!
Lectures
| Day | Sections | Content |
|---|---|---|
| 24/3 | Introduction to the course, to time series and stationarity | |
| 26/3 | [BD] 2.1, 2.4 | Characterization of stationarity |
| 31/3 | [BD] 1.6, 2.5 | Hypothesis testing, forecasting time series |
| 2/4 | [BD] 2.5 | Forecasting stationary time series |
| 21/4 | [BD] 1.5, 2.2 | Trend and seasonality, linear processes |
| 23/4 | [BD] 3.1-3.2 | ARMA processes (causality, invertibility, ACVF) |
| 28/4 | [BD] 3.1-3.2, 5.1-5.2 | ARMA processes (PACF, parameter estimation) |
| 5/5 | [BD] 3.3, 5.3-5.5, 6.1, 6.3 | ARMA processes (order identification, model building, forecasting, ARIMA, unit roots) |
| 7/5 | [BD] 7.1-7.2, [T] 3 | (G)ARCH processes |
| 12/5 | [BD] 7.1-7.3, [T] 3 | (G)ARCH processes |
| 14/5 | [T] 3.6, 4.1 [BD] 7.3 | IGARCH processes, nonlinear models |
| 19/5 | [T] 4.1, 4.2 | Nonparametric models, testing for nonlinearities |
| 26/5 | [T] 4.4 | Nonparametric forecasting |
| 28/5 | Old exams and course evaluation |
Recommended exercises
| Day | Exercises |
|---|---|
| 27/3 | [BD] 1.1, 1.3*, 1.4, 1.6, 1.7 and exercises in basic probability, at least 1-4. One or two of these extra exercises will also be covered on the blackboard. |
| 3/4 | [BD] 2.1, 2.2, 2.3, 2.4, 2.7, 2.8, 2.14ab, 2.15, 2.20, 2.21 |
| 24/4 | [BD] 1.10, 1.11, 1.12a, 1.13, 1.15, 3.1abcde, 3.3abcde, 3.6, 3.7, 3.8. |
| 8/5 | [BD] 3.4, 3.11, 5.3, 5.4abde, 5.8, 5.11, 5.12, 6.1. |
| 15/5 | [BD] 1.8 and additional ARCH and GARCH exercises (2, 4, and 6 will be covered). |
| 29/5 | Non-linear model exercises. |
Exercises in bold will be covered on the blackboard during excercise sessions. You are expected to try to solve them on your own before the session.
* You may assume that the process has densities associated with its finite dimensional distributions.
Projects
Course summary:
| Date | Details | Due |
|---|---|---|