290435 PS Applied Time Series Analysis (2016S)
Continuous assessment of course work
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This course is taught in English. The lecturer speaks German too, so don't be scared.
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Sa 13.02.2016 08:00 to Tu 23.02.2016 23:59
- Registration is open from Th 25.02.2016 13:15 to We 02.03.2016 23:00
- Deregistration possible until Th 31.03.2016 23:59
Details
max. 25 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 09.03. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
- Wednesday 16.03. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
- Wednesday 13.04. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
- Wednesday 20.04. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
- Wednesday 04.05. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
- Wednesday 08.06. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
- Wednesday 15.06. 17:00 - 21:00 Seminarraum Geographie NIG 5.OG C0528
Information
Aims, contents and method of the course
Assessment and permitted materials
The grading is based on the following.) A short multiple choice test at the end of the semester (35%)
.) The presentation of the project work (35%)
.) Homework (30%)
.) The presentation of the project work (35%)
.) Homework (30%)
Minimum requirements and assessment criteria
Minimum requirements for a positive mark: 50% of total score and positive end test.
Examination topics
Main LiteratureShumway, R. and Stoffer, D. (2010). Time Series Analysis and Its Applications: With R Examples. Springer Texts in Statistics. Springer.
Reading list
(see above)Further LiteratureBrockwell, P. J. and Davis, R. A. (2006). Introduction to time series and forecasting. Springer Science & Business Media.Hackl, P. (2008). Einführung in die Ökonometrie. Pearson Studium – Economic BWL. Pearson Studium.James, G., Witten, D., Hastie, T., and Tibshirani, R. (2014). An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics. Springer New York.Kleiber, C. and Zeileis, A. (2008). Applied Econometrics with R. Springer.Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2015). Introduction to time
series analysis and forecasting. John Wiley & Sons.Wooldridge, J. (2009). Introductory Econometrics: A Modern Approach: A Modern Approach. ISE - International Student Edition. South Western, Cengage Learning.Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT press.
series analysis and forecasting. John Wiley & Sons.Wooldridge, J. (2009). Introductory Econometrics: A Modern Approach: A Modern Approach. ISE - International Student Edition. South Western, Cengage Learning.Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT press.
Association in the course directory
(MG-W3-PI) (MG-W5-PI) (MG-W6-PI) (MR1-PI) (MR6-PI) (L2-FW) (D5)
Last modified: Mo 07.09.2020 15:42
The course covers theory that is applied to real data and is therefore primarily focused on applications and the acquisition of practical skills.1. Recap: linear regression and its assumptions; introduction to R
2. Characteristics of Time Series & Stationarity
3. Time Series Regression and Exploratory Data Analysis
4. ARIMA Models
5. Spectral Analysis and Filtering
6. Unit Root Testing
7. Additional Time Domain TopicsStudents are also invited to BYOP (bring your own problems) to class early on.Each unit comprises a theoretical part and an applied part. In the applied part students foster their new theoretic knowledge by testing real world data in the statistical software R. For this second part computer equipment is necessary. Students will work on a project of their choice which they will present to the class in the later part of the course. The class will then discuss the work.