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#70250 / #4

Seit WS 2019/20


Time Series Analysis


Werwatz, Axel


Schriftliche Prüfung


Fakultät VII

Institut für Volkswirtschaftslehre und Wirtschaftsrecht

37312100 FG Ökonometrie und Wirtschaftsstatistik



H 57

Plitzko, Franziska


Learning Outcomes

Most economic variables, if measured over time, show serial dependence. That is, current realizations of the variable depend on past outcomes. Capturing this serial dependence with a statistical model and utilizing this statistical model to forecast future values of the variable are the main goals of this course. For this purpose, students will learn identification, estimation, diagnostic checking and forecasting of ARIMA models. Key ingredients of the course are the weekly tutorials that focus on hands-on experience in applying these models to actual data in a computer classroom.


Descriptive and explorative methods (exponential smoothing). Stationarity and the Autocorrelation Function. Autoregressive Moving-Average (ARMA) Models and their properties. identification, estimation, diagnostic checking and forecasting of ARMA models. Non-stationarity, ARIMA models and unit root tests, Seasonal ARIMA models.

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Time Series AnalysisVL71 210 L 1616WiSeEnglish2
Time Series AnalysisUE71 210 L 1617WiSeEnglish2

Workload and Credit Points

Time Series Analysis (VL):

Workload descriptionMultiplierHoursTotal
60.0h(~2 LP)
Class attendance15.02.0h30.0h
Pre/post processing15.02.0h30.0h

Time Series Analysis (UE):

Workload descriptionMultiplierHoursTotal
60.0h(~2 LP)
Class attendance15.02.0h30.0h
Pre/post processing15.02.0h30.0h

Course-independent workload:

Workload descriptionMultiplierHoursTotal
60.0h(~2 LP)
Exam preparation1.060.0h60.0h
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Lecture and Exercise. Exercises take place at the computer lab where real or simulated data and the statistics software package STATA is used. An introduction to STATA will be given at the beginning of the course (Übung).

Requirements for participation and examination

Desirable prerequisites for participation in the courses:


Mandatory requirements for the module test application:

1. Requirement
Modul70232 passed
2. Requirement
Modul70231 passed

Module completion



Type of exam

Written exam




The exam will take 90min.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Please note the information on our website (http://www.statistik.tu-berlin.de).

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  available
Additional information:
Script will be uploaded on the ISIS system and is protected by a passwort



Recommended literature
Hamilton, J.D. (1994). Time Series Analysis, Princeton University Press.
Kirchgässner, G. und Wolters, J. (2006). Einführung in die moderne Zeitreihenanalyse, Vahlen

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Industrial Economics (M. Sc.)17WS 2019/20WiSe 2022/23
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)212WS 2019/20SoSe 2023
Soziologie technikwissenschaftlicher Richtung (B. A.)18WS 2019/20SoSe 2023
Wirtschaftsingenieurwesen (M. Sc.)19WS 2019/20SoSe 2023

Students of other degrees can participate in this module without capacity testing.


No information