Modulhandbuch (Module manual)

M.184.5453 Advanced Time Series Analysis and Forecasting
(Advanced Time Series Analysis and Forecasting)

Koordinator (coordinator):	Prof. Dr. Yuanhua Feng
Ansprechpartner (contact):	Sebastian Letmathe (lettron[at]mail.uni-paderborn.de) Prof. Dr. Yuanhua Feng (yuanhua.feng[at]uni-paderborn.de) Shujie Li (shujie.li[at]uni-paderborn.de) Dominik Schulz (dominik.schulz[at]upb.de)
Credits:	5 ECTS
Workload:	150 Std (h)
Semesterturnus (semester cycle):	WS
Studiensemester (study semester):	1-4
Dauer in Semestern (duration in semesters):	1

Lehrveranstaltungen (courses):
	Nummer / Name (number / title)	Art (type)	Kontaktzeit (contact time)	Selbststudium (self-study)	Status (P/WP) (status)	Gruppengröße (group size)
a)	K.184.54531 / Advanced Time Series Analysis and Forecasting	Vorlesung			P
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Keine

Empfohlene Voraussetzungen (prerequisites):
You should have visited one of the following modules: W4451 Financial Econometrics and Quantitative Risk Managment W2453 Angewandte Zeitreihenanalyse und Einführung in die Finanzökonometrie or a comparable time series course at another university

Empfohlene Voraussetzungen (prerequisites):

You should have visited one of the following modules:

W4451 Financial Econometrics and Quantitative Risk Managment

W2453 Angewandte Zeitreihenanalyse und Einführung in die Finanzökonometrie

or a comparable time series course at another university

Inhalte (short description):
This is an advanced lecture in time series analysis developed based on basic knowledge in time series. Hence, one of our Master modules W4451 or Bachelor modules W2453, or a comparable module that you have visited at another university is a necessary requirement. The main topics of this module will be divided into two parts: Part 1: Advanced linear time series models, including the analysis of time series with seasonality and different calendar effects, multivariate time series models as well as long memory time series models; and Part 2: advanced topics of non-linear and functional time series, including long memory volatility and duration models, multivariate volatility and correlation models, volatility and correlation measures based on high-frequency financial data as well as the analysis of functional time series with short or long memory. The focuses are on the introduction of the theory and methods, practical implementation in R and their application in forecasting and decision making. Practical implementation in Python will be discussed as well, given that suitable Python packages are available. Application to economic and financial time series, particularly in sustainable economics and finance, will be strongly emphasised. Semiparametric extensions of corresponding approaches under non-stationary component time series models will be described as far as possible.

Inhalte (short description):

This is an advanced lecture in time series analysis developed based on basic knowledge in time series. Hence, one of our Master modules W4451 or Bachelor modules W2453, or a comparable module that you have visited at another university is a necessary requirement. The main topics of this module will be divided into two parts: Part 1: Advanced linear time series models, including the analysis of time series with seasonality and different calendar effects, multivariate time series models as well as long memory time series models; and Part 2: advanced topics of non-linear and functional time series, including long memory volatility and duration models, multivariate volatility and correlation models, volatility and correlation measures based on high-frequency financial data as well as the analysis of functional time series with short or long memory. The focuses are on the introduction of the theory and methods, practical implementation in R and their application in forecasting and decision making. Practical implementation in Python will be discussed as well, given that suitable Python packages are available. Application to economic and financial time series, particularly in sustainable economics and finance, will be strongly emphasised. Semiparametric extensions of corresponding approaches under non-stationary component time series models will be described as far as possible.

Lernergebnisse (learning outcomes):

Fachkompetenz Wissen (professional expertise):
Studierende... Advanced knowledge in time series and forecasting; advanced R/Python skills
Fachkompetenz Fertigkeit (practical professional and academic skills):
Studierende... can use advanced computational tools and sophisticated modern statistical approaches for illustrating and modeling and analysing different kinds of data gain skills to analyze big multivariate and functional data sets gain further knowledge about the programming language R and basic knowledge of Python improve their computing, data illustration and data management skills improve their analytical and empirical study skills
Personale Kompetenz / Sozial (individual competences / social skills):
Studierende... cooperate and work in groups ability for carrying out a practically relevant project deep understanding of environmental time series
Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
Studierende... gain more expertise and skills in scientific working and writing gain strong skills in modern data analysis and data science are further trained in independent and research related studying

Prüfungsleistungen (examinations)
Art der Modulprüfung (type of modul examination): Keine Modulprüfung
	Art der Prüfung (type of examination)	Umfang (extent)	Gewichtung (weighting)
a)	Projektarbeit	10 - 15 pages	50.00 %
b)	Projektarbeit	10 - 15 pages	50.00 %

Studienleistung / qualifizierte Teilnahme (module participation requirements)
Nein

Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
Keine

Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist

Gewichtung für Gesamtnote (calculation of overall grade)
Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)

Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
M.Sc. IBS, M.Sc. BWL, M.Sc. International Economics and Management, M.Sc. Management, M.Sc. Management Information Systems, M.Sc. Taxation, Accountingand Finance, M.Sc. Winfo, M.Sc. Wirtschaftspädagogik, M.Ed. Wirtschaftspädagogik M.Sc. International Economics and Management

Umfang QT (participation requirements):

Lernmaterialien, Literaturangaben (learning material, literature):
Selected References: The main Text: Fuller (1996). Introduction to Statistical Time Series. Tsay (2010). Analysis of Financial Time Series. John Wiley. Beran, J., Feng, Y., Ghosh, S., Kulik, R. (2013). Long Memory Processes. Springer. Hautsch (2012). Econometrics of Financial High-Frequency Data. Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer Ghosh, S. (2018). Kernel Smoothing: Principles, Methods and Applications. Wiley. Ramsay, J.O, Silverman, B.W. (2005). Functional Data Analysis. Springer. Bowman, A, Azzalini, A. (2001). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford. Selected readings on functional time series analysis as well as on semiparametric regression.

Lernmaterialien, Literaturangaben (learning material, literature):

Selected References:

The main Text:

Fuller (1996). Introduction to Statistical Time Series.

Tsay (2010). Analysis of Financial Time Series. John Wiley.

Beran, J., Feng, Y., Ghosh, S., Kulik, R. (2013). Long Memory Processes. Springer.

Hautsch (2012). Econometrics of Financial High-Frequency Data.

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer

Ghosh, S. (2018). Kernel Smoothing: Principles, Methods and Applications. Wiley.

Ramsay, J.O, Silverman, B.W. (2005). Functional Data Analysis. Springer.

Bowman, A, Azzalini, A. (2001). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford.

Selected readings on functional time series analysis as well as on semiparametric regression.

Teilnehmerbegrenzung (participant limit):
Keine

Sonstige Hinweise (additional information):
keine Teilnehmerbeschränkung

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