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Time Series Analysis

Announcements:
 

  • The review of the retake exam will take place on 26 November 2013 from 11:30 to 12:00 in room 2312.

 

  • The PC pool on the first floor in KG II (room 2114a) is open as follows:
    Monday and Tuesday 13:00-19:00
    Wednesday 14:00-16:00/18:00 (not every week)

 

 

Instructur (PC pool seesions): Jennifer Stapornwongkul 

Lecture (Sevtap Kestel, Ph.D.):
Mo., 08.07.2013, 08:00-10:00, HS 1009
Mo., 08.07.2013, 10:00-12:00, HS 1021
Tue., 09.07.2013, 08:00-10:00, HS 3043
Tue., 09.07.2013, 10:00-12:00, Breisacher Tor, room 101
Wed.,10.07.2013, 08:00-10:00, HS 3043
Wed.,10.07.2013, 10:00-12:00, HS 1009
Thu., 11.07.2013, 08:00-10:00, HS 1228
Thu., 11.07.2013, 10:00-12:00, HS 1199
Fri., 12.07.2013, 08:00-12:00, HS 3042
 


Tutorial/Office hour (Sevtap Kestel, Ph.D.):

Tue., 16.07.2013, 10:00-12:00, HS 1019


PC pool sessions:
Thu., 11.07.2013, 12:00-14:00, room -1001B
Fri., 12.07.2013, 16:00-18:00, room -1001B
Sat., 13.07.2013, 09:00-12:00, room -1001B


Credits:
4 CP


Language:
The course is taught in English.
 

Participants:
Approximately 40
 

Exam:
Tuesday, 30 July, 16:00-18:00 (90 minutes), HS 3044
Retake: Tuesday, 15 October, 16:00-18:00 (90 minutes), HS 3044
 
 

Course Material


Lecture Script

Exercise (Classical TS)

 

Lecture Slides:

Review 

Classical Time Series NEW

Stationary Time Series NEW

Assignment  (Due to: July 30, 16:00h)

Assignment Text

Groups of two participants should submit  one set, both in printed and soft versions. Draft should be handed in to the Proctor in duty at the Final Examination.

Please also send the copy of the Assignment  to Dr. Kestel by email by the deadline stated below (sevtap.kestel@vwl.uni-freiburg.de).

Deadline:

Latest by Tuesday, 30 July, 16:00, draft (printed) version  to be submitted to the proctor in the Final Examination.


Data  Set:

ALL GROUPS SHOULD WORK ON THE SAME DATA SETS WHICH ARE GIVEN BELOW.

Data Set 1 (Daily)
Data Set 2 (Monthly)



Course Outline

The study of the sequence of data points measured at successive times enables us to often either to understand the underlying theory of the data points (where did they come from what generated them), or to make forecasts (predictions). Time series prediction is the use of a model to predict future events based on known past events: to predict future data points before they are measured.

The objective of the course is to provide students to learn time series modelling in theory and practice. The course will start with reviewing the fundamental concepts in regression analysis. Autocorrelation function, Linear Stationary models: General linear process, Autoregressive, Moving averages, ARMA processes, Non-stationary models: Autoregressive Integrated Moving Average and Integrated Moving Average processes, Forecasting: Minimum Mean Square Error Forecast, updating forecasts, Stochastic Model building: Model identification, Model estimation (maximum likelihood estimation, nonlinear estimation, Bayes’ estimation), Model diagnostic checking, Seasonal models,  Vector Autoregressive Models, and cointegration will be covered.

 

Literature

  • Enders, W., Applied Econometric Time Series, Second Edition, Wiley
  • Kirchgässner, Wolters, Introduction to Modern Time Series, First Edition, Springer Verlag

 

 

Kontakt

Abteilung für Empirische Wirtschaftsforschung und Ökonometrie

Raum 2309

Platz der Alten Synagoge

D-79085 Freiburg

Tel.: 0761 203-2338

Fax: 0761 203-2340

Benutzerspezifische Werkzeuge