Leibniz Universität Hannover Faculty Economics and Management
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Institute of Statistics
 
Institute of Statistics Teaching
Lectures

Lectures of the Institute of Statistics

All following courses are described by semesters. For seminars, there are detailed fact sheets where you may find more information.

Overview Bachelor

Compulsory Program

Module / EventRecom. SemesterLanguageSemester
Statistics (Descriptive Statistics) 1German Winter
Statistics (Inductive Statistics) 2GermanSummer

Area BWL / VWL

Module / EventRecom. SemesterLanguageSemester
Econometrics5German Winter
Seminar Econometrics 6German Summer

Overview Master

Area Empirical Economics and Econometrics

Module / EventRecom. SemesterLanguageSemester
Advanced Statistics1 EnglishWinter
Advanced Econometrics1EnglishWinter
Computerintensive Statistics3English irregular
Financial Econometrics13English Winter
Multivariate Statistics2English Summer
Nonparametric Statistical Methods2 English Summer
Statistical Programming2 English Summer
Stochastic Processes for Option Pricing3 EnglishWinter
Time Series Analysis12 English Summer
Advanced Time Series Analysis13 English Winter
Seminar Econometrics3 English Winter
Seminar Applied Econometrics2 English Summer

1 also Area Finance, Banking & Insurance

Event Announcements by Semester

  • Summer term 2025

    Bachelor Wirtschaftswissenschaft

    Kompetenzbereich Statistik

    • Schließende Statistik (270158)

      Termine:Lehrpersonen:
      Di. 09:15 - 10:45 | VII-201Sibbertsen
      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-201Sibbertsen

    • Tutorium zu Schließende Statistik (270031)

      Termine:Lehrpersonen:
      Mo. 09:15 - 10:45 | I-442 (Gruppe 1)Tutor
      Mo. 09:15 - 10:45 | I-332 (Gruppe 2)Tutor
      Mo. 09:15 - 10:45 | VII-004 (Gruppe 3)Tutor
      Mo. 14:30 - 16:00 | VII-002 (Gruppe 4)Tutor
      Mo. 16:15 - 17:45 | I-442 (Gruppe 5)Tutor
      Di. 11:00 - 12:30 | I-442 (Gruppe 6)Tutor
      Di. 12:45 - 14:15 | I-401 (Gruppe 7)Tutor
      Di. 12:45 - 14:15 | I-442 (Gruppe 8)Tutor
      Di. 12:45 - 14:15 | I-112 (Gruppe 9)Tutor
      Di. 14:30 - 16:00 | I-112 (Gruppe 10)Tutor
      Mi. 11:00 - 12:30 | I-063 (Gruppe 11)Tutor
      Mi. 12:45 - 14:15 | I-442 (Gruppe 12)Tutor
      Mi. 12:45 - 14:15 | I-112 (Gruppe 13)Tutor
      Mi. 14:30 - 16:00 | I-442 (Gruppe 14)Tutor
      Mi. 16:15 - 17:45 | VII-004 (Gruppe 15)Tutor
      Do. 09:15 - 10:45 | I-342 (Gruppe 16)Tutor
      Do. 11:00 - 12:30 | I-301 (Gruppe 17)Tutor
      Do. 11:00 - 12:30 | VII-005 (Gruppe 18)Tutor
      Do. 11:00 - 12:30 | I-063 (Gruppe 19)Tutor
      Do. 16:15 - 17:45 | I-063 (Gruppe 20)Tutor
      Fr. 09:15 - 10:45 | I-332 (Gruppe 21)Tutor
      Fr. 09:15 - 10:45 | I-063 (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 vom 8.4.2025 16:30 bis 11.4.2025 23:59 statt.

    Master Wirtschaftswissenschaft

    Kompetenzbereich (Area) Empirical Economics and Econometrics

    • Statistical Programming (373005)

      Termine:Lehrpersonen:
      Mo. 14:30 - 16:00 | I-233Kreye
      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:
      Mi. 14:30 - 16:00 | I-063Less
      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:
      Mo. 12:45 - 14:15 | I-063Fitter
      Inhalt:
      • Short overview of Matrix and Vector Algebra
      • Multivariate descriptive statistics
      • Multivariate Distributions
      • Multivariate analysis of variance
      • Introduction to data analysis
      • 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:
      Mi. 12:45 - 14:15 | I-063Yu
      Inhalt:
      • Metropolis Algorithm
      • Adaptive Metropolis Algorithm
      • Delayed Rejection Adaptive Metropolis
      • Metropolis-Hastings Algorithm
      • Gibbs Sampling
      Literatur:

      Albert, Jim (2007): Bayesian Computation with R, Springer

      J.S Urban Hjorth(1994) Computer Intensive Statistical Methods

    • Machine Learning (373024)

      Termine:Lehrpersonen:
      Mi. 09:15 - 10:45 | I-342Toumping 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:
      Di. 14:30 - 16:00 | I-442Sibbertsen
      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-401Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, 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.

    Forschungsveranstaltungen

    • Research Seminar Financial Markets and the Global Challenges (77782)

      Termine:Lehrpersonen:
      Mi. 11:00 - 12:30 | I-442Dierkes, Dräger, Gassebner, Prokopczuk, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
      Inhalt:

      External guests present their latest research

  • Winter term 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:
      1. Einführung
      2. Empirische Verteilungen
      3. Korrelationsrechnung
      4. Wahrscheinlichkeitsrechnung
      5. 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-301Kreye
      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-063Sibbertsen
      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-063Toumping 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-342Fitter
      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-063Yu
      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:
      BlockveranstaltungGassebner, 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-442Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Schneider, Schröder, Sibbertsen, Todtenhaupt
      Inhalt:

      External guests present their latest research

  • Summer term 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-214Kreye
      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-063Fitter
      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-342Toumping 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-442Yu
      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-401Blaufus, 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:
      BlockveranstaltungSibbertsen
      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-442Blaufus, 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:
      1. Einführung
      2. Empirische Verteilungen
      3. Korrelationsrechnung
      4. Wahrscheinlichkeitsrechnung
      5. 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-301Kreye
      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-063Sibbertsen
      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-063Toumping 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-063Less
      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-342Fitter
      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-063Yu
      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:
      BlockveranstaltungGassebner, Prokopczuk, Sibbertsen

    Forschungsveranstaltungen

    • Research Seminar Financial Markets and the Global Challenges (77782)

      Termine:Lehrpersonen:
      Mi. 11:00 - 12:30 | I-442Blaufus, 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-063Sibbertsen
      Inhalt:

      Im Kolloquium werden Forschungsprojekte im Rahmen des Forschungsschwerpunkts "Innovation und Lernen" vorgestellt.

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