Lehrveranstaltungen – Institut für Statistik – Leibniz Universität Hannover

Übersicht Bachelor

Pflichtprogramm

Modul / Veranstaltung	Empf. Fachsemester	Sprache	Semester
Statistik (Beschreibende Statistik)	1	Deutsch	Winter
Statistik (Schließende Statistik)	2	Deutsch	Sommer

Kompetenzbereich BWL / VWL

Modul / Veranstaltung	Empf. Fachsemester	Sprache	Semester
Ökonometrie	5	Deutsch	Winter
Seminar Ökonometrie	6	Deutsch	Sommer

Übersicht Master

Area Empirical Economics and Econometrics

Modul / Veranstaltung	Empf. Fachsemester	Sprache	Semester
Advanced Statistics	1	Englisch	Winter
Advanced Econometrics	1	Englisch	Winter
Computerintensive Statistics	3	Englisch	unregelmäßig
Financial Econometrics¹	3	Englisch	Winter
Multivariate Statistics	2	Englisch	Sommer
Nonparametric Statistical Methods	2	Englisch	Sommer
Statistical Programming	2	Englisch	Sommer
Stochastic Processes for Option Pricing	3	Englisch	Winter
Time Series Analysis¹	2	Englisch	Sommer
Advanced Time Series Analysis¹	3	Englisch	Winter
Seminar Econometrics	3	Englisch	Winter
Seminar Applied Econometrics	2	Englisch	Sommer

¹ Auch Area Finance, Banking & Insurance

Veranstaltungsankündigungen nach Semestern

Wintersemester 2024/2025

Bachelor Wirtschaftswissenschaft

Kompetenzbereich Statistik

Tutorium zu Beschreibende Statistik (270024)

	Termine:	Lehrpersonen:
	Mo. 09:15 - 10:45 \| I-332 (Gruppe 1)	Tutor
	Mo. 09:15 - 10:45 \| I-112 (Gruppe 2)	Tutor
	Mo. 12:45 - 14:15 \| I-342 (Gruppe 3)	Tutor
	Mo. 12:45 - 14:15 \| I-112 (Gruppe 4)	Tutor
	Mo. 16:15 - 17:45 \| I-442 (Gruppe 5)	Tutor
	Mo. 16:15 - 17:45 \| I-112 (Gruppe 6)	Tutor
	Di. 11:00 - 12:30 \| I-063 (Gruppe 7)	Tutor
	Di. 16:15 - 17:45 \| I-112 (Gruppe 8)	Tutor
	Di. 18:15 - 19:45 \| I-112 (Gruppe 9)	Tutor
	Mi. 07:30 - 09:00 \| VII-004 (Gruppe 10)	Tutor
	Mi. 09:15 - 10:45 \| III-115 (Gruppe 11)	Tutor
	Mi. 09:15 - 10:45 \| I-112 (Gruppe 12)	Tutor
	Mi. 12:45 - 14:15 \| III-115 (Gruppe 13)	Tutor
	Mi. 12:45 - 14:15 \| I-112 (Gruppe 14)	Tutor
	Mi. 14:30 - 16:00 \| I-332 (Gruppe 15)	Tutor
	Do. 07:30 - 09:00 \| I-063 (Gruppe 16)	Tutor
	Do. 11:00 - 12:30 \| I-063 (Gruppe 17)	Tutor
	Do. 11:00 - 12:30 \| I-112 (Gruppe 18)	Tutor
	Do. 16:15 - 17:45 \| I-112 (Gruppe 19)	Tutor
	Fr. 07:30 - 09:00 \| I-063 (Gruppe 20)	Tutor
	Fr. 09:15 - 10:45 \| I-063 (Gruppe 21)	Tutor
	Fr. 09:15 - 10:45 \| VII-005 (Gruppe 22)	Tutor
	Fr. 14:30 - 16:00 \| I-063 (Gruppe 23)	Tutor
	Fr. 14:30 - 16:00 \| VII-004 (Gruppe 24)	Tutor
Bemerkungen:	Es wird Bereitschaft zur aktiven Mitarbeit erwartet. Es handelt sich um ein ergänzendes Tutorium in Präsenzform. Termine und organisatorische Einzelheiten werden in der Vorlesung und über das StudIP bekannt gegeben. Beginn der Gruppenanmeldung in Stud.IP: Di. 22.10.2024 16:45 Uhr Ende der Gruppenanmeldung in Stud.IP: Mi. 30.10.2024 23:59 Uhr

Beschreibende Statistik (270148)

	Termine:	Lehrpersonen:
	Di. 09:15 - 10:45 \| VII-201 (Gruppe 1)	Sibbertsen
	Di. 09:15 - 10:45 \| VII-002 (Gruppe 2)	Sibbertsen
Inhalt:	Einführung Empirische Verteilungen Korrelationsrechnung Wahrscheinlichkeitsrechnung Theoretische Verteilungen.
Literatur:	Sibbertsen, P., Lehne, H. (2015) Statistik, Einführung für Wirtschafts- und Sozialwissenschaftler, Springer, Berlin. Schira, J. (2009) Statistische Methoden der VWL und BWL, Pearson München, 3. Auflage. Fahrmeir et al (2009) Statistik: Der Weg zur Datenanalyse, Springer, Berlin, 7. Auflage. Bamberg, Baur (2001) Statistik, Oldenbourg, München, 12. Auflage.

Übung zu Beschreibende Statistik (270150)

	Termine:	Lehrpersonen:
	Di. 07:30 - 09:00 \| VII-201 (Gruppe 1)	Sibbertsen
	Di. 07:30 - 09:00 \| VII-002 (Gruppe 2)	Sibbertsen
Bemerkungen:	Endet nach Hälfte der Vorlesungszeit

Schließende Statistik für Wiederholer (270028)

	Termine:	Lehrpersonen:
	Mo. 09:15 - 10:45 \| I-301	Kreye
Bemerkungen:	Das Wiederholungstutorium beginnt in der ersten Vorlesungswoche. Der letzte Termin wird in der Woche vor den Wiederholungsprüfungen stattfinden.

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

Seminar Ökonometrie (273002)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	Blockveranstaltung (Gruppe 2)	Less
Inhalt:	Thema des Seminars im Sommersemester 2024 ist "Regressionsanalyse"
Bemerkungen:	Das Seminar wird als Blockveranstaltung durchgeführt. Nähere Angaben zur Themenvergabe und zum Zeitpunkt der Veranstaltung werden auf der Internetseite des Instituts für Statistik bekannt gegeben. Prüfer: Prof. Dr. Sibbertsen

Master Wirtschaftswissenschaft

Kompetenzbereich (Area) Empirical Economics and Econometrics

Advanced Statistics (373000)

	Termine:	Lehrpersonen:
	Di. 12:45 - 14:15 \| I-063	Sibbertsen
Inhalt:	Probability Theory: Random Variables, Densities Distribution Functions, Moments of Random Variables Parametric Families of Distributions Point Estimation: Least Squares, Method of Moments, GMM, Maximum Likelihood Hypothesis Testing: Theory of Testing, LR-, Wald-, LM-Test, Testing in the linear model
Literatur:	Mood, Graybill and Boes (1974) Introduction to the Theory of Statistics. Mittelhammer, R. (1996): Mathematical Statistics for Economics and Business, Springer.

Seminar Econometrics (373002)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	(Gruppe 2)	Less
Inhalt:	The seminar will be on "Time Series Analysis"
Bemerkungen:	Further Information about the seminar can be found on the website of the Institute for Statistics. The seminar will be held as a face-to-face event. Prüfer: Prof. Dr. Sibbertsen

Statistical Database Management (373029)

	Termine:	Lehrpersonen:
	Mi. 09:15 - 10:45 \| I-063	Toumping Fotso
Inhalt:	This course aims to enable students to create relational databases, write SQL statements to extract information from databases to satisfy business reporting requests, create ERD To design databases, visualize the design of ERD, and analyze table design for excessive redundancy. The students will also be able to use common aggregate query operators in SQL and more sophisticated statistical measures may also be supported by some database systems.
Literatur:	Date, Chris J. Database design and relational theory: normal forms and all that jazz. Apress, 2019. Silberschatz, Abraham, Henry F. Korth, and S. Sudarshan. "System Concepts." (2008). Gupta, A. (2009). Statistical Data Management. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1290.
Bemerkungen:	List of software and packages required for the course: MySQL server, MySQL workbench, ERD design tool

Mehrere Kompetenzbereiche (Areas)

Advanced Econometrics (379023)

	Termine:	Lehrpersonen:
	Do. 11:00 - 12:30 \| I-342	Fitter
Inhalt:	Introduction to Basic Econometrics & R Probit & Logit Models Count Data Models Tobit & Selection Models Estimating Treatment Effects Survival Analysis
Literatur:	Takeshi Amemiya. Advanced Econometrics. Harvard university press, 1985. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications, Cambridge University Press. Greene, W. H., 2012. Econometric Analysis, Pearson. Hayashi, F., Econometrics, 2000. Princeton University Press. Stock, J. H., Watson, M. W., 2014. Introduction to Econometrics, Pearson. Wooldridge, J. M., 2010. Econometric Analysis of Cross Section and Panel Data, MIT Press. Wooldridge, J. M., 2012. Introductory Econometrics, South-Western College Publishing.
Bemerkungen:	More information on Stud.IP

Advanced Time Series Analysis (379029)

	Termine:	Lehrpersonen:
	Do. 14:30 - 16:00 \| I-063	Yu
Inhalt:	Introduction and overview Multivariate Time Series Models Vector Autoregressive Models (VARs) and structural VARs Cointegration and Error-Correction Models Vector Error-Correction Models Non-linear models and Breaks Threshold Autoregressive Models (TAR) Extension of TAR Models
Literatur:	Enders, W. (2014). Applied Econometric Time Series, Wiley. Lütkepohl, H. (2005) New Introduction to Multiple Time Series Analysis, Springer. Lütkepohl, H. (2004) Applied Time Series Econometrics, Cambridge University Press.

Promotionsstudium

1. Bereich: Fachliche Kompetenzen

Advanced Econometric Topics for Finance and Economics (571001)

	Termine:	Lehrpersonen:
	Blockveranstaltung	Gassebner, Prokopczuk, Sibbertsen
Inhalt:	Interested PhD stdutens should register via email by 30 October 2024 at sekretariat@fcm.uni-hannover.de. Please include your matriculation number. The course will be held in blocks.

Termine:

Lehrpersonen:

Blockveranstaltung

Gassebner, Prokopczuk, Sibbertsen

Inhalt:

Interested PhD stdutens should register via email by 30 October 2024 at sekretariat@fcm.uni-hannover.de. Please include your matriculation number.

The course will be held in blocks.

Forschungsveranstaltungen

Research Seminar Financial Markets and the Global Challenges (77782)

	Termine:	Lehrpersonen:
	Mi. 11:00 - 12:30 \| I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Schneider, Schröder, Sibbertsen, Todtenhaupt
Inhalt:	External guests present their latest research

Sommersemester 2024

Bachelor Wirtschaftswissenschaft

Kompetenzbereich Statistik

Tutorium zu Schließende Statistik (270031)

	Termine:	Lehrpersonen:
	Mo. 09:15 - 10:45 \| II-013 (Gruppe 1)	Tutor
	Mo. 09:15 - 10:45 \| I-442 (Gruppe 2)	Tutor
	Mo. 12:45 - 14:15 \| I-342 (Gruppe 3)	Tutor
	Mo. 12:45 - 14:15 \| I-442 (Gruppe 4)	Tutor
	Mo. 12:45 - 14:15 \| I-063 (Gruppe 5)	Tutor
	Di. 11:00 - 12:30 \| I-442 (Gruppe 6)	Tutor
	Di. 11:00 - 12:30 \| I-063 (Gruppe 7)	Tutor
	Di. 16:15 - 17:45 \| I-063 (Gruppe 8)	Tutor
	Mi. 11:00 - 12:30 \| VII-004 (Gruppe 9)	Tutor
	Mi. 11:00 - 12:30 \| I-342 (Gruppe 10)	Tutor
	Mi. 12:45 - 14:15 \| I-332 (Gruppe 11)	Tutor
	Mi. 12:45 - 14:15 \| III-115 (Gruppe 12)	Tutor
	Mi. 14:30 - 16:00 \| III-115 (Gruppe 13)	Tutor
	Mi. 16:15 - 17:45 \| VII-004 (Gruppe 14)	Tutor
	Do. 09:15 - 10:45 \| VII-005 (Gruppe 15)	Tutor
	Do. 11:00 - 12:30 \| I-332 (Gruppe 16)	Tutor
	Do. 11:00 - 12:30 \| I-063 (Gruppe 17)	Tutor
	Do. 14:30 - 16:00 \| I-301 (Gruppe 18)	Tutor
	Fr. 09:15 - 10:45 \| I-342 (Gruppe 19)	Tutor
	Fr. 09:15 - 10:45 \| I-332 (Gruppe 20)	Tutor
	Fr. 11:00 - 12:30 \| I-342 (Gruppe 21)	Tutor
	Fr. 11:00 - 12:30 \| I-442 (Gruppe 22)	Tutor
	Fr. 11:00 - 12:30 \| I-332 (Gruppe 23)	Tutor
	Fr. 11:00 - 12:30 \| I-063 (Gruppe 24)	Tutor
Bemerkungen:	Bereitschaft zur aktiven Mitarbeit in den eingeteilten Tutoriumsgruppen wird erwartet. Die Gruppeneinteilung findet über Stud.IP am Di. 16.04.2024 ab 11:30 Uhr statt.

Schließende Statistik (270158)

	Termine:	Lehrpersonen:
	Di. 09:15 - 10:45 \| VII-201 (Gruppe 1)	Sibbertsen
	Di. 09:15 - 10:45 \| VII-002 (Gruppe 2)	Sibbertsen
Inhalt:	Normalverteilung Binomialverteilung Stichproben Punktschätzung Intervallschätzung Statistische Tests Regressionsanalyse
Literatur:	Sibbertsen, P./Lehne, H. (2014) Statistik, 2. Auflage, Berlin. Fahrmeir, L. et al. (2004) Statistik, 5. Auflage Berlin. Schlittgen, R. (2003) Einführung in die Statistik, 10. Auflage München.

Übung zu Schließende Statistik (270159)

Termine: Lehrpersonen:

Di. 07:30 - 09:00 | VII-201 (Gruppe 1) Sibbertsen

Di. 07:30 - 09:00 | VII-002 (Gruppe 2) Sibbertsen

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

Seminar Ökonometrie (273002)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	(Gruppe 2)	NN(Statistik), Toumping Fotso
Inhalt:	Thema des Seminars im Sommersemester 2024 ist "Regressionsanalyse"
Bemerkungen:	Das Seminar wird als Blockveranstaltung durchgeführt. Nähere Angaben zur Themenvergabe und zum Zeitpunkt der Veranstaltung werden auf der Internetseite des Instituts für Statistik bekannt gegeben. Prüfer: Prof. Dr. Sibbertsen

Master Wirtschaftswissenschaft

Kompetenzbereich (Area) Empirical Economics and Econometrics

Statistical Programming (373005)

	Termine:	Lehrpersonen:
	Do. 11:00 - 12:30 \| II-214	Kreye
Inhalt:	Data Structures Functions and Loops Handling Data Graphics Linear Regression Numerical Optimization Monte Carlo Methods
Literatur:	Ligges (2007) Programmieren mit R, Berlin, Springer. Braun / Murdock (2007) A first course in statistical programming with R, Cambridge University Press. Rizzo (2008) Statistical Computing with R, Chapman & Hall.

Multivariate Statistics (373011)

	Termine:	Lehrpersonen:
	Do. 09:15 - 10:45 \| I-063	Fitter
Inhalt:	Short overview of Matrix and Vector Algebra Multivariate descriptive statistics Multivariate normaldistribution Multivariate analysis of variance Introduction to data analysis Discriminant Analysis and Classification Cluster Analysis Principal Components Analysis and Factor Analysis.
Literatur:	Izenman, A. J. (2008). Modern multivariate statistical techniques. Regression, classification and manifold learning. Johnson R. A. and Wichern D. W., (2007), Applied Multivariate Statistical Analysis. 6th edition. New Jersey: Pearson. Krzanowski, W. (2000). Principles of multivariate analysis (Vol. 23). OUP Oxford. Rencher, A. C. and Christensen W. F. (2012). Methods of multivariate analysis.3rd edition. John Wiley & Sons.

Machine Learning (373024)

	Termine:	Lehrpersonen:
	Mi. 09:15 - 10:45 \| I-342	Toumping Fotso
Inhalt:	The term machine learning summarises a wide range of statistical methods used for pattern recognition, classification and prediction. Applications encompass the recognition of text, speech and images, spam and fraud detection, recommendation systems for customers, as well as generating information from large quantities of data or predicting stock prices. This lecture covers a selection of common supervised to unsupervised learning algorithms. These refer to clustering and regression problems, and clustering and dimensionality reduction methods respectively. Examples of covered statistical methods include: Linear and logistic regression K-nearest neighbours Naïve Bayes Model selection and cross validation Tree-based methods Support vector machines Principal component analysis Neural networks The lecture includes applications in Python. Previous experience with Python or R is helpful, but not required.
Literatur:	Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer. Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Termine:

Lehrpersonen:

Mi. 09:15 - 10:45 | I-342

Toumping Fotso

Inhalt:

The term machine learning summarises a wide range of statistical methods used for pattern recognition, classification and prediction. Applications encompass the recognition of text, speech and images, spam and fraud detection, recommendation systems for customers, as well as generating information from large quantities of data or predicting stock prices.

This lecture covers a selection of common supervised to unsupervised learning algorithms. These refer to clustering and regression problems, and clustering and dimensionality reduction methods respectively. Examples of covered statistical methods include:

Linear and logistic regression
K-nearest neighbours
Naïve Bayes
Model selection and cross validation
Tree-based methods
Support vector machines
Principal component analysis
Neural networks

The lecture includes applications in Python. Previous experience with Python or R is helpful, but not required.

Literatur:

Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer.
Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Mehrere Kompetenzbereiche (Areas)

Time Series Analysis (379016)

	Termine:	Lehrpersonen:
	Mo. 14:30 - 16:00 \| I-442	Yu
Inhalt:	Stationarity Autoregressive und Moving Average Models Non-Stationarity Forecasting Spectral Analysis Long Memory Time Series.
Literatur:	Hamilton, J. D. (1994): Time Series Analysis, Princeton. Schlittgen, R., Stritberg, H. J. (2003): Zeitreihenanalyse, Oldenbourg.

Lecture Series: Financial Markets and the Global Challenges (379059)

	Termine:	Lehrpersonen:
	Di. 16:15 - 17:45 \| I-401	Blaufus, Dierkes, Dräger, Foege, Gassebner, Prokopczuk, Schneider, Schöndube, Schröder, Sibbertsen, Todtenhaupt
Inhalt:	Financial markets are the backbone of the economy. The world is facing many challenges such as climate change, crime and international conflicts, ageing societies or economic disruptions. In this lecture series, faculty members of the School of Economics and Management will discuss how financial markets are related and/or might provide means to tackle these challenges.

Promotionsstudium

3. Bereich: Wissenschaftliche Kompetenzen

Doktorandenseminar Statistik (574004)

Termine: Lehrpersonen:

Blockveranstaltung Sibbertsen

Inhalt:
The students present and discuss their own latest research results.

Literatur:
Given in the seminar

	Termine:	Lehrpersonen:
	Blockveranstaltung	Sibbertsen
Inhalt:	The students present and discuss their own latest research results.
Literatur:	Given in the seminar

Forschungsveranstaltungen

Research Seminar Financial Markets and the Global Challenges (77782)

	Termine:	Lehrpersonen:
	Mi. 11:00 - 12:30 \| I-442	Blaufus, Dierkes, Dräger, Foege, Gassebner, Prokopczuk, Schneider, Schöndube, Schröder, Sibbertsen, Todtenhaupt
Inhalt:	External guests present their latest research

Wintersemester 2023/2024

Bachelor Wirtschaftswissenschaft

Kompetenzbereich Statistik

Tutorium zu Beschreibende Statistik (270024)

	Termine:	Lehrpersonen:
	Mo. 12:45 - 14:15 \| I-442 (Gruppe 1)	Tutor
	Mi. 12:45 - 14:15 \| II-013 (Gruppe 2)	Tutor
	Mo. 12:45 - 14:15 \| I-063 (Gruppe 3)	Tutor
	Mo. 16:15 - 17:45 \| I-442 (Gruppe 4)	Tutor
	Mo. 16:15 - 17:45 \| I-332 (Gruppe 5)	Tutor
	Mo. 16:15 - 17:45 \| VII-004 (Gruppe 6)	Tutor
	Di. 16:15 - 17:45 \| I-332 (Gruppe 7)	Tutor
	Mi. 09:15 - 10:45 \| VII-004 (Gruppe 8)	Tutor
	Mi. 09:15 - 10:45 \| I-401 (Gruppe 9)	Tutor
	Mi. 12:45 - 14:15 \| II-214 (Gruppe 10)	Tutor
	Mi. 12:45 - 14:15 \| I-063 (Gruppe 11)	Tutor
	Mi. 14:30 - 16:00 \| I-442 (Gruppe 12)	Tutor
	Do. 07:30 - 09:00 \| I-063 (Gruppe 13)	Tutor
	Do. 11:00 - 12:30 \| VII-005 (Gruppe 14)	Tutor
	Di. 16:15 - 17:45 \| VII-004 (Gruppe 15)	Tutor
	Do. 12:45 - 14:15 \| I-063 (Gruppe 16)	Tutor
	Mi. 18:15 - 19:45 \| II-013 (Gruppe 17)	Tutor
	Do. 16:15 - 17:45 \| I-063 (Gruppe 18)	Tutor
	Fr. 07:30 - 09:00 \| I-063 (Gruppe 19)	Tutor
	Mi. 14:30 - 16:00 \| II-214 (Gruppe 20)	Tutor
	Fr. 11:00 - 12:30 \| VII-005 (Gruppe 21)	Tutor
	Fr. 11:00 - 12:30 \| I-063 (Gruppe 22)	Tutor
	Fr. 14:30 - 16:00 \| VII-004 (Gruppe 23)	Tutor
	Fr. 14:30 - 16:00 \| I-063 (Gruppe 24)	Tutor
Bemerkungen:	Es wird Bereitschaft zur aktiven Mitarbeit erwartet. Es handelt sich um ein ergänzendes Tutorium in Präsenzform. Termine und organisatorische Einzelheiten werden in der Vorlesung und über das StudIP bekannt gegeben. Beginn der Gruppenanmeldung in Stud.IP: Do. 19.10.2023 13:00 Uhr Ende der Gruppenanmeldung in Stud.IP: Fr. 27.10.2023 14:00 Uhr

Beschreibende Statistik (270148)

	Termine:	Lehrpersonen:
	Di. 09:15 - 10:45 \| VII-201 (Gruppe 1)	Sibbertsen
	Do. 09:15 - 10:45 \| VII-002 (Gruppe 2)	Lehne
Inhalt:	Einführung Empirische Verteilungen Korrelationsrechnung Wahrscheinlichkeitsrechnung Theoretische Verteilungen.
Literatur:	Sibbertsen, P., Lehne, H. (2015) Statistik, Einführung für Wirtschafts- und Sozialwissenschaftler, Springer, Berlin. Schira, J. (2009) Statistische Methoden der VWL und BWL, Pearson München, 3. Auflage. Fahrmeir et al (2009) Statistik: Der Weg zur Datenanalyse, Springer, Berlin, 7. Auflage. Bamberg, Baur (2001) Statistik, Oldenbourg, München, 12. Auflage.

Übung zu Beschreibende Statistik (270150)

	Termine:	Lehrpersonen:
	Di. 07:30 - 09:00 \| VII-201 (Gruppe 1)	Sibbertsen
	Mo. 09:15 - 10:45 \| VII-002 (Gruppe 2)	Lehne
Bemerkungen:	Endet nach Hälfte der Vorlesungszeit

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

Ökonometrie (273006)

	Termine:	Lehrpersonen:
	Mo. 16:15 - 17:45 \| I-301	Kreye
Inhalt:	Einführung, mathematische und statistische Grundlagen Allgemeines multiples lineares Regressionsmodell Erweiterungen und Anwendungen des linearen Regressionsmodells: Fehlspezifikation, Modellwahl, Modelldiagnose, Multikollinearität, stochasitsche Regressoren
Literatur:	Stock, J.H. and M.W. Watson (2007): Introduction to Econometrics, 3rd ed. Pearson. von Auer, L. (2003): Okonometrie. Eine Einführung, 2. Aufl. Springer Verlag. Verbeek, L. (2012): A Guide to Modern Econometrics, 4th ed. Wiley.
Bemerkungen:	Materialien werden auf Stud.IP zur Verfügung gestellt.

Master Wirtschaftswissenschaft

Kompetenzbereich (Area) Empirical Economics and Econometrics

Advanced Statistics (373000)

	Termine:	Lehrpersonen:
	Di. 12:45 - 14:15 \| I-063	Sibbertsen
Inhalt:	Probability Theory: Random Variables, Densities Distribution Functions, Moments of Random Variables Parametric Families of Distributions Point Estimation: Least Squares, Method of Moments, GMM, Maximum Likelihood Hypothesis Testing: Theory of Testing, LR-, Wald-, LM-Test, Testing in the linear model
Literatur:	Mood, Graybill and Boes (1974) Introduction to the Theory of Statistics. Mittelhammer, R. (1996): Mathematical Statistics for Economics and Business, Springer.

Seminar Econometrics (373002)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	(Gruppe 2)	NN(Statistik)
Inhalt:	The seminar will be on "Time Series Analysis"
Bemerkungen:	Further Information about the seminar can be found on the website of the Institute for Statistics. The seminar will be held as a face-to-face event. Prüfer: Prof. Dr. Sibbertsen

Statistical Database Management (373029)

	Termine:	Lehrpersonen:
	Mi. 07:30 - 09:00 \| I-063	Toumping Fotso
Inhalt:	This course aims to enable students to create relational databases, write SQL statements to extract information from databases to satisfy business reporting requests, create ERD To design databases, visualize the design of ERD, and analyze table design for excessive redundancy. The students will also be able to use common aggregate query operators in SQL and more sophisticated statistical measures may also be supported by some database systems.
Literatur:	Date, Chris J. Database design and relational theory: normal forms and all that jazz. Apress, 2019. Silberschatz, Abraham, Henry F. Korth, and S. Sudarshan. "System Concepts." (2008). Gupta, A. (2009). Statistical Data Management. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1290.
Bemerkungen:	List of software and packages required for the course: MySQL server, MySQL workbench, ERD design tool

Mehrere Kompetenzbereiche (Areas)

Financial Econometrics (379012)

	Termine:	Lehrpersonen:
	Do. 14:30 - 16:00 \| I-063	Less
Inhalt:	Characteristics of Financial Time Series Volatility Modelling Factor Models Cointegration Empirical Tests of the CAPM.
Literatur:	Andersen, T. G., Davis, R. A., Kreiss, J. P., & Mikosch, T. V. (Eds.). (2009): Handbook of financial time series. Springer Science & Business Media. Campbell, J. Y., Lo, A. W. and MacKinlay, A. C. (1997): The Econometrics of Financial Markets, Princeton University Press, Princeton, New Jersey. Greene, W. H. (2012): Econometric analysis (International Edition), 7th ed., Pearson, Essex. Hamilton, J. D. (1994): Time Series Analysis, Princeton University Press, Princeton, New Jersey. Martin, V., Hurn, S. and Harris, D. (2013): Econometric Modelling with Time Series - Specification, Estimation and Testing, Cambridge University Press, New York, USA. Tsay, R. S. (2010): Analysis of Financial Time Series, 3rd. ed., Wiley, Hoboken, New Jersey.

Advanced Econometrics (379023)

	Termine:	Lehrpersonen:
	Di. 09:15 - 10:45 \| I-342	Fitter
Inhalt:	Introduction to Basic Econometrics & R Probit & Logit Models Count Data Models Tobit & Selection Models Estimating Treatment Effects Survival Analysis
Literatur:	Takeshi Amemiya. Advanced Econometrics. Harvard university press, 1985. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications, Cambridge University Press. Greene, W. H., 2012. Econometric Analysis, Pearson. Hayashi, F., Econometrics, 2000. Princeton University Press. Stock, J. H., Watson, M. W., 2014. Introduction to Econometrics, Pearson. Wooldridge, J. M., 2010. Econometric Analysis of Cross Section and Panel Data, MIT Press. Wooldridge, J. M., 2012. Introductory Econometrics, South-Western College Publishing. Mario Cleves et al. An Introduction to Survival Analysis using Stata. Vol. 3. Stata press, 2010.
Bemerkungen:	More information on Stud.IP

Advanced Time Series Analysis (379029)

	Termine:	Lehrpersonen:
	Fr. 09:15 - 10:45 \| I-063	Yu
Inhalt:	Introduction and overview Multivariate Time Series Models Vector Autoregressive Models (VARs) and structural VARs Cointegration and Error-Correction Models Vector Error-Correction Models Non-linear models and Breaks Threshold Autoregressive Models (TAR) Extension of TAR Models
Literatur:	Enders, W. (2014). Applied Econometric Time Series, Wiley. Lütkepohl, H. (2005) New Introduction to Multiple Time Series Analysis, Springer. Lütkepohl, H. (2004) Applied Time Series Econometrics, Cambridge University Press.

Promotionsstudium

1. Bereich: Fachliche Kompetenzen

Advanced Econometric Topics for Finance and Economics (571001)

Termine: Lehrpersonen:

Blockveranstaltung Gassebner, Prokopczuk, Sibbertsen

	Termine:	Lehrpersonen:
	Blockveranstaltung	Gassebner, Prokopczuk, Sibbertsen

Forschungsveranstaltungen

Research Seminar Financial Markets and the Global Challenges (77782)

	Termine:	Lehrpersonen:
	Mi. 11:00 - 12:30 \| I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Schneider, Schöndube, Schröder, Sibbertsen, Todtenhaupt
Inhalt:	External guests present their latest research

Kolloquium Innovation und Lernen (77787)

	Termine:	Lehrpersonen:
	Do. 11:00 - 12:30 \| I-063	Sibbertsen
Inhalt:	Im Kolloquium werden Forschungsprojekte im Rahmen des Forschungsschwerpunkts "Innovation und Lernen" vorgestellt.

Sommersemester 2023

Bachelor Wirtschaftswissenschaft

Kompetenzbereich Statistik

Tutorium zu Schließende Statistik (270031)

	Termine:	Lehrpersonen:
	Mo. 12:45 - 14:15 \| I-342 (Gruppe 1)	Tutor
	Mo. 12:45 - 14:15 \| I-442 (Gruppe 2)	Tutor
	Mo. 12:45 - 14:15 \| I-332 (Gruppe 3)	Tutor
	Mo. 14:30 - 16:00 \| I-442 (Gruppe 4)	Tutor
	Mo. 14:30 - 16:00 \| I-332 (Gruppe 5)	Tutor
	Mo. 14:30 - 16:00 \| I-063 (Gruppe 6)	Tutor
	Di. 09:15 - 10:45 \| II-013 (Gruppe 7)	Tutor
	Di. 14:30 - 16:00 \| I-442 (Gruppe 8)	Tutor
	Di. 16:15 - 17:45 \| I-063 (Gruppe 9)	Tutor
	Mi. 11:00 - 12:30 \| VII-004 (Gruppe 10)	Tutor
	Mi. 11:00 - 12:30 \| I-063 (Gruppe 11)	Tutor
	Mi. 14:30 - 16:00 \| I-342 (Gruppe 12)	Tutor
	Mi. 14:30 - 16:00 \| I-332 (Gruppe 13)	Tutor
	Do. 09:15 - 10:45 \| VII-005 (Gruppe 14)	Tutor
	Do. 09:15 - 10:45 \| I-442 (Gruppe 15)	Tutor
	Do. 09:15 - 10:45 \| I-332 (Gruppe 16)	Tutor
	Do. 11:00 - 12:30 \| I-342 (Gruppe 17)	Tutor
	Do. 11:00 - 12:30 \| VII-005 (Gruppe 18)	Tutor
	Do. 14:30 - 16:00 \| I-063 (Gruppe 19)	Tutor
	Do. 16:15 - 17:45 \| I-063 (Gruppe 20)	Tutor
	Fr. 11:00 - 12:30 \| I-342 (Gruppe 21)	Tutor
	Fr. 11:00 - 12:30 \| I-332 (Gruppe 22)	Tutor
	Fr. 11:00 - 12:30 \| I-063 (Gruppe 23)	Tutor
	Fr. 14:30 - 16:00 \| I-332 (Gruppe 24)	Tutor
Bemerkungen:	Bereitschaft zur aktiven Mitarbeit in den eingeteilten Tutoriumsgruppen wird erwartet. Die Gruppeneinteilung findet über Stud.IP am Mi. 19.04.2023 ab 11:30 Uhr statt. Die Gruppen 1, 8, 21 und 23 müssen leider entfallen.

Schließende Statistik (270158)

	Termine:	Lehrpersonen:
	Mi. 09:15 - 10:45 \| VII-002 (Gruppe 1)	Sibbertsen
	Mo. 09:15 - 10:45 \| VII-201 (Gruppe 2)	Lehne
Inhalt:	Normalverteilung Binomialverteilung Stichproben Punktschätzung Intervallschätzung Statistische Tests Regressionsanalyse
Literatur:	Sibbertsen, P./Lehne, H. (2014) Statistik, 2. Auflage, Berlin. Fahrmeir, L. et al. (2004) Statistik, 5. Auflage Berlin. Schlittgen, R. (2003) Einführung in die Statistik, 10. Auflage München.

Übung zu Schließende Statistik (270159)

Termine: Lehrpersonen:

Mi. 07:30 - 09:00 | VII-002 (Gruppe 1) Sibbertsen

Fr. 09:15 - 10:45 | VII-201 (Gruppe 2) Lehne

	Termine:	Lehrpersonen:
	Mi. 07:30 - 09:00 \| VII-002 (Gruppe 1)	Sibbertsen
	Fr. 09:15 - 10:45 \| VII-201 (Gruppe 2)	Lehne

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

Seminar Ökonometrie (273002)

	Termine:	Lehrpersonen:
	Blockveranstaltung (Gruppe 1)	Sibbertsen
	Blockveranstaltung (Gruppe 2)	Flock, Toumping Fotso
Inhalt:	Thema des Seminars im Sommersemester 2023 ist "Regressionsanalyse"
Bemerkungen:	Das Seminar wird als Blockveranstaltung durchgeführt. Nähere Angaben zur Themenvergabe und zum Zeitpunkt der Veranstaltung werden auf der Internetseite des Instituts für Statistik bekannt gegeben. Prüfer: Prof. Dr. Sibbertsen

Master Wirtschaftswissenschaft

Kompetenzbereich (Area) Empirical Economics and Econometrics

Statistical Programming (373005)

	Termine:	Lehrpersonen:
	Do. 12:45 - 14:15 \| II-214	Flock
Inhalt:	Data Structures Functions and Loops Handling Data Graphics Linear Regression Numerical Optimization Monte Carlo Methods
Literatur:	Ligges (2007) Programmieren mit R, Berlin, Springer. Braun / Murdock (2007) A first course in statistical programming with R, Cambridge University Press. Rizzo (2008) Statistical Computing with R, Chapman & Hall.

Nonparametric Statistical Methods (373010)

	Termine:	Lehrpersonen:
	Mo. 09:15 - 10:45 \| I-063	Less
Inhalt:	Kernel density estimation Nonparametric regression Semiparametric methods Machine learning
Literatur:	Härdle, W. (1992) Applied Nonparametric Regression, Cambridge University Press. Henderson, D. J., Parmeter, C. F. (2015) Applied Nonparametric Econometrics, Cambridge University Press. Li, Q., Racine, J. S. (2007) Nonparametric Econometrics, Princeton University Press. Pagan, A., Ullah A. (1999): Nonparametric Econometrics, Cambridge University Press. Friedman, J., Hastie, T., Tibshirani, R., (2001): The Elements of Statistical Learning, Springer.

Multivariate Statistics (373011)

	Termine:	Lehrpersonen:
	Di. 09:15 - 10:45 \| I-063	Fitter
Inhalt:	Short overview of Matrix and Vector Algebra Multivariate descriptive statistics Multivariate normaldistribution Multivariate analysis of variance Introduction to data analysis Discriminant Analysis and Classification Cluster Analysis Principal Components Analysis and Factor Analysis.
Literatur:	Izenman, A. J. (2008). Modern multivariate statistical techniques. Regression, classification and manifold learning. Johnson R. A. and Wichern D. W., (2007), Applied Multivariate Statistical Analysis. 6th edition. New Jersey: Pearson. Krzanowski, W. (2000). Principles of multivariate analysis (Vol. 23). OUP Oxford. Rencher, A. C. and Christensen W. F. (2012). Methods of multivariate analysis.3rd edition. John Wiley & Sons.

Computerintensive Statistics (373015)

	Termine:	Lehrpersonen:
	Fr. 07:30 - 09:00 \| I-063	Toumping Fotso
Inhalt:	Metropolis Algorithm Adaptive Metropolis Algorithm Delayed Rejection Adaptive Metropolis Metropolis-Hastings Algorithm Gibbs Sampling
Literatur:	Albert, Jim (2007): Bayesian Computation with R, Springer

Machine Learning (373024)

	Termine:	Lehrpersonen:
	Do. 09:15 - 10:45 \| I-342	Meier
Inhalt:	The term machine learning summarises a wide range of statistical methods used for pattern recognition, classification and prediction. Applications encompass the recognition of text, speech and images, spam and fraud detection, recommendation systems for customers, as well as generating information from large quantities of data or predicting stock prices. This lecture covers a selection of common supervised to unsupervised learning algorithms. These refer to clustering and regression problems, and clustering and dimensionality reduction methods respectively. Examples of covered statistical methods include: Linear and logistic regression K-nearest neighbours Naïve Bayes Model selection and cross validation Tree-based methods Support vector machines Principal component analysis Neural networks The lecture includes applications in Python. Previous experience with Python or R is helpful, but not required.
Literatur:	Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer. Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Termine:

Lehrpersonen:

Do. 09:15 - 10:45 | I-342

Meier

Inhalt:

Linear and logistic regression
K-nearest neighbours
Naïve Bayes
Model selection and cross validation
Tree-based methods
Support vector machines
Principal component analysis
Neural networks

The lecture includes applications in Python. Previous experience with Python or R is helpful, but not required.

Literatur:

Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer.
Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Mehrere Kompetenzbereiche (Areas)

Time Series Analysis (379016)

	Termine:	Lehrpersonen:
	Di. 07:30 - 09:00 \| I-342	Sibbertsen
Inhalt:	Stationarity Autoregressive und Moving Average Models Non-Stationarity Forecasting Spectral Analysis Long Memory Time Series.
Literatur:	Hamilton, J. D. (1994): Time Series Analysis, Princeton. Schlittgen, R., Stritberg, H. J. (2003): Zeitreihenanalyse, Oldenbourg.

Ringvorlesung Financial Markets and the Global Challenges (379059)

	Termine:	Lehrpersonen:
	Di. 16:15 - 17:45 \| I-342	Blaufus, Dräger, Gassebner, Gnutzmann-Mkrtchyan, Prokopczuk, Schneider, Schöndube, Schröder, Sibbertsen, Todtenhaupt
Inhalt:	Financial markets are the backbone of the economy. The world is facing many challenges such as climate change, crime and international conflicts, ageing societies or economic disruptions. In this lecture series, faculty members of the School of Economics and Management will discuss how financial markets are related and/or might provide means to tackle these challenges. After attending the lecture series, students can pick one specific topic and write a term paper (Hausarbeit) supervised by the corresponding faculty member.

Promotionsstudium

3. Bereich: Wissenschaftliche Kompetenzen

Doktorandenseminar Statistik (574004)

Termine: Lehrpersonen:

Blockveranstaltung Sibbertsen

Inhalt:
The students present and discuss their own latest research results.

Literatur:
Given in the seminar

Forschungsveranstaltungen

Research Seminar Financial Markets and the Global Challenges (77782)

	Termine:	Lehrpersonen:
	Mi. 11:00 - 12:30 \| I-442	Blaufus, Dierkes, Dräger, Gassebner, Gnutzmann-Mkrtchyan, Prokopczuk, Schneider, Schöndube, Schröder, Sibbertsen, Todtenhaupt
Inhalt:	External guests present their latest research

Kolloquium Innovation und Lernen (77787)

	Termine:	Lehrpersonen:
	Blockveranstaltung	Blaufus, Dierkes, Dräger, Foege, Gassebner, Gnutzmann-Mkrtchyan, Grote, Haunschild, Piening, Prokopczuk, Schneider, Schöndube, Schröder, Sibbertsen, Walsh, Weber, Wielenberg
Inhalt:	Im Kolloquium werden Forschungsprojekte im Rahmen des Forschungsschwerpunkts "Innovation und Lernen" vorgestellt.

Lehrveranstaltungen des Instituts für Statistik

Übersicht Bachelor

Pflichtprogramm

Kompetenzbereich BWL / VWL

Übersicht Master

Area Empirical Economics and Econometrics

Veranstaltungsankündigungen nach Semestern