Moses - Time Series Analysis

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WS 2019/20 - SoSe 2023. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#70250 / #3

Validity:
WS 2019/20 - SoSe 2023

Default display language:
English

Module title:
Time Series Analysis

Credits:
6

Responsible person:
Werwatz, Axel

Grading:
benotet

Type of exam:
Schriftliche Prüfung

Zugehörigkeit

Faculty:
Fakultät VII

Institute:
Institut für Volkswirtschaftslehre und Wirtschaftsrecht

Area of expertise:
37312100 FG Ökonometrie und Wirtschaftsstatistik

Prüfungsausschuss:
Volkswirtschaftslehre

Kontakt

Office:
H 57

Contact person:
Plitzko, Franziska

E-mail address:
axel.werwatz@tu-berlin.de

Website:

http://www.statistik.tu-berlin.de

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	WiSe	English	2
Time Series Analysis	UE	71 210 L 1617	WiSe	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 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

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

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

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

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

Miscellaneous

No information

Time Series Analysis