Navigation To modulepage
Display language

Time Series Analysis

6

English

#70250 / #4

Seit WS 2019/20

Fakultät VII

H 57

Institut für Volkswirtschaftslehre und Wirtschaftsrecht

37312100 FG Ökonometrie und Wirtschaftsstatistik

Werwatz, Axel

Plitzko, Franziska

axel.werwatz@tu-berlin.de

POS-Nummer PORD-Nummer Modultitel
120380 19106 Time Series Analysis

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.

Content

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

Pflichtgruppe:

All Courses are mandatory.

Course Name Type Number Cycle Language SWS VZ
Time Series Analysis VL 71 210 L 1616 WS English 2
Time Series Analysis UE 71 210 L 1617 WS English 2

Workload and Credit Points

Time Series Analysis (VL):

Workload description Multiplier Hours Total
Class attendance 15.0 2.0h 30.0h
Pre/post processing 15.0 2.0h 30.0h
60.0h(~2 LP)

Time Series Analysis (UE):

Workload description Multiplier Hours Total
Class attendance 15.0 2.0h 30.0h
Pre/post processing 15.0 2.0h 30.0h
60.0h(~2 LP)

Course-independent workload:

Workload description Multiplier Hours Total
Exam preparation 1.0 60.0h 60.0h
60.0h(~2 LP)
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:

Ökonometrie

Mandatory requirements for the module test application:

1. Requirement
Modul70232 [Statistik II für Wirtschaftswissenschaften] passed
2. Requirement
Modul70231 [Statistik I für Wirtschaftswissenschaften] passed

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

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:
Wintersemester.

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

Literature

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):

Verwendungen (2)
Studiengänge: 2 Stupos: 2 Erstes Semester: WS 2019/20 Letztes Semester: offen

This moduleversion is used in the following modulelists:

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

Miscellaneous

No information