Lectures of the Institute of Statistics

Area of Expertise in Statistics

• Tutorial on statistical inference (270031)

  	Time and room:	Lecturer:
  	Mon. 09:15 - 10:45 | I-332 (Group 1)	Tutor
  	Mon. 09:15 - 10:45 | I-442 (Group 2)	Tutor
  	Mon. 09:15 - 10:45 | VII-004 (Group 3)	Tutor
  	Mon. 14:30 - 16:00 | I-442 (Group 4)	Tutor
  	Mon. 16:15 - 17:45 | I-442 (Group 5)	Tutor
  	Tue. 11:00 - 12:30 | I-342 (Group 6)	Tutor
  	Tue. 12:45 - 14:15 | I-342 (Group 7)	Tutor
  	Tue. 12:45 - 14:15 | III-115 (Group 8)	Tutor
  	Wed. 09:15 - 10:45 | I-332 (Group 9)	Tutor
  	Wed. 11:00 - 12:30 | I-342 (Group 10)	Tutor
  	Wed. 11:00 - 12:30 | I-063 (Group 11)	Tutor
  	Wed. 12:45 - 14:15 | I-342 (Group 12)	Tutor
  	Wed. 12:45 - 14:15 | I-442 (Group 13)	Tutor
  	Wed. 12:45 - 14:15 | I-063 (Group 14)	Tutor
  	Wed. 14:30 - 16:00 | II-013 (Group 15)	Tutor
  	Thu. 09:15 - 10:45 | I-063 (Group 16)	Tutor
  	Thu. 11:00 - 12:30 | VII-005 (Group 17)	Tutor
  	Thu. 11:00 - 12:30 | I-063 (Group 18)	Tutor
  	Thu. 14:30 - 16:00 | I-342 (Group 19)	Tutor
  	Thu. 16:15 - 17:45 | I-063 (Group 20)	Tutor
  	Fri. 09:15 - 10:45 | I-332 (Group 21)	Tutor
  	Fri. 14:30 - 16:00 | I-442 (Group 22)	Tutor
  	Fri. 14:30 - 16:00 | I-063 (Group 23)	Tutor
  	Mon. 16:15 - 17:45 | VII-004 (Group 24)	Tutor
  Comments:	Active participation in the assigned tutorial groups is expected. Group registration will take place via Stud.IP, starting on Tuesday, 07 April 2026 (first lecture) at 12:30 p.m. and ending on Tuesday, 14 April 2026 at 11:59 p.m.

• Statistical Inference (270158)

  	Time and room:	Lecturer:
  	Tue. 09:15 - 10:45 | VII-201	Sibbertsen
  Contents:	Normal Distribution Binomial Distribution Sampling Point Estimation Intervall Estimation Statistical Testing Linear Regression
  Literature:	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.

• Statistical Inference (270159)

  	Time and room:	Lecturer:
  	Tue. 07:30 - 09:00 | VII-201	Sibbertsen

Area Data Science and Applied Econometrics

• Statistical Programming (373005)

  	Time and room:	Lecturer:
  	Thu. 16:15 - 17:45 | II-214	Rogge
  Contents:	Data Structures Functions and Loops Handling Data Graphics Linear Regression Numerical Optimization Monte Carlo Methods
  Literature:	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)

  	Time and room:	Lecturer:
  	Tue. 14:30 - 16:00 | I-063	Less
  Contents:	Kernel density estimation Nonparametric regression Semiparametric methods Machine learning
  Literature:	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.

• Computerintensive Statistics (373015)

  	Time and room:	Lecturer:
  	Wed. 14:30 - 16:00 | I-063	Yu
  Contents:	Markov chain Metropolis Algorithm Adaptive Metropolis Algorithm Delayed Rejection Adaptive Metropolis Metropolis-Hastings Algorithm Monte Carlo Bootstrapping Markov Chain Monte Carlo
  Literature:	Albert, Jim (2007), Bayesian Computation with R, Springer J.S Urban Hjorth(1994), Computer Intensive Statistical Methods, Physica Heidelberg, 1993. W.R.Gilks, S, Richardson and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice. Chapman & Hall / CRC, USA, 1996.
  Comments:	The course is divided into two main sections: lectures and R exercises.

• Machine Learning (373024)

  	Time and room:	Lecturer:
  	Wed. 09:15 - 10:45 | VII-004	Toumping Fotso
  Contents:	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. Linear and logistic regression XGBoost, AdaBoost, Gradient Boosting Regression and classification trees Model selection and cross-validation Support vector machines Neural networks (maybe) The lecture includes applications in Python. Previous experience with Python or R is helpful, but not required.
  Literature:	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.

Several Areas

• Time Series Analysis (379016)

  	Time and room:	Lecturer:
  	Tue. 11:00 - 12:30 | I-063	Sibbertsen
  Contents:	Stationarity Autoregressive und Moving Average Models Non-Stationarity Forecasting Spectral Analysis Long Memory Time Series.
  Literature:	Hamilton, J. D. (1994): Time Series Analysis, Princeton. Schlittgen, R., Stritberg, H. J. (2003): Zeitreihenanalyse, Oldenbourg.

• Finance Research Seminar (77782)

  	Time and room:	Lecturer:
  	Wed. 11:00 - 12:30 | I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
  Contents:	External guests present their latest research

Kompetenzbereich Statistik

• Tutorium zu Beschreibende Statistik (270024)

  	Termine:	Lehrpersonen:
  	Mo. 09:15 - 10:45 | I-301 (Gruppe 1)	Tutor
  	Mo. 09:15 - 10:45 | II-013 (Gruppe 2)	Tutor
  	Mo. 09:15 - 10:45 | I-442 (Gruppe 3)	Tutor
  	Mo. 12:45 - 14:15 | I-442 (Gruppe 4)	Tutor
  	Mo. 12:45 - 14:15 | VII-004 (Gruppe 5)	Tutor
  	Mo. 16:15 - 17:45 | I-332 (Gruppe 6)	Tutor
  	Mo. 16:15 - 17:45 | VII-004 (Gruppe 7)	Tutor
  	Di. 11:00 - 12:30 | I-401 (Gruppe 8)	Tutor
  	Di. 16:15 - 17:45 | I-442 (Gruppe 9)	Tutor
  	Mi. 11:00 - 12:30 | II-013 (Gruppe 10)	Tutor
  	Mi. 12:45 - 14:15 | VII-004 (Gruppe 11)	Tutor
  	Mi. 12:45 - 14:15 | I-063 (Gruppe 12)	Tutor
  	Mi. 14:30 - 16:00 | I-401 (Gruppe 13)	Tutor
  	Mi. 14:30 - 16:00 | I-063 (Gruppe 14)	Tutor
  	Do. 09:15 - 10:45 | I-332 (Gruppe 15)	Tutor
  	Do. 11:00 - 12:30 | I-401 (Gruppe 16)	Tutor
  	Do. 11:00 - 12:30 | I-342 (Gruppe 17)	Tutor
  	Do. 14:30 - 16:00 | I-342 (Gruppe 18)	Tutor
  	Do. 14:30 - 16:00 | I-332 (Gruppe 19)	Tutor
  	Do. 16:15 - 17:45 | I-332 (Gruppe 20)	Tutor
  	Fr. 09:15 - 10:45 | I-401 (Gruppe 21)	Tutor
  	Fr. 09:15 - 10:45 | II-013 (Gruppe 22)	Tutor
  	Fr. 11:00 - 12:30 | I-401 (Gruppe 23)	Tutor
  	Fr. 14:30 - 16:00 | I-342 (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. 21.10.2025 16:45 Uhr Ende der Gruppenanmeldung in Stud.IP: Mi. 29.10.2025 23:59 Uhr

• Schließende Statistik für Wiederholer (270028)

  	Termine:	Lehrpersonen:
  	Mo. 11:00 - 12:30 (14-tägig) | VII-004 Fr. 16:15 - 17:45 (14-tägig) | I-342	Fitter
  Bemerkungen:	Das Wiederholungstutorium beginnt in der ersten Vorlesungswoche. Der letzte Termin wird in der Woche vor den Wiederholungsprüfungen stattfinden.

• Beschreibende Statistik (270148)

  	Termine:	Lehrpersonen:
  	Di. 09:15 - 10:45 | VII-201	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	Sibbertsen
  Bemerkungen:	Endet nach Hälfte der Vorlesungszeit

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

• Seminar Ökonometrie (273002)

  	Termine:	Lehrpersonen:
  	Blockveranstaltung (Gruppe 1)	Sibbertsen
  	Blockveranstaltung (Gruppe 2)	Özer
  Inhalt:	Thema des Seminars im Wintersemester 2025 / 2026 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

• Ökonometrie (273006)

  	Termine:	Lehrpersonen:
  	Do. 14:30 - 16:00 | I-301	Less
  Inhalt:	Die folgenden Kursinhalte sind geplant, können aber je nach zeitlichem Verlauf variieren: Motivationen für das lineare Regressionsmodell Schätzung und Inferenz Endogenität: Ursprünge und Lösungen Das verallgemeinerte lineare Regressionsmodell Instrumentenvariablen Das Verständnis für die theoretischen Konzepte aus der Vorlesung wird in der Übung gefördert. Dies geschiet u.a. durch den Einsatz statistischer Software. Die Übung ist somit ein wichtiger Bestandteil des Moduls.
  Literatur:	Angrist, J. und Pischke, J.-S. (2015), Mastering Metrics: The path from cause to effect, Princeton University Press, Princeton, New Jersey. Hansen, B. (2022), Econometrics, Princeton University Press, Princeton, New Jersey. Stock, J.H. and M.W. Watson (2007): Introduction to Econometrics, 3rd ed. Pearson
  Bemerkungen:	Materialien werden sukzessive auf Stud.IP zur Verfügung gestellt.

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:
  	Mo. 09:15 - 10:45 | I-063	Rogge
  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
  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.
  Bemerkungen:	This lecture focuses on multivariate time series analysis. As such, it involves working with vectors and matrices, both in theory and computation. A solid understanding of basic statistics and mathematics is therefore essential for success in this lecture. The lecture will include the necessary vector and matrix computations.

1. Bereich: Fachliche Kompetenzen

• Advanced Econometric Topics for Finance and Economics (571001)

  	Termine:	Lehrpersonen:
  	Blockveranstaltung	Gassebner, Prokopczuk, Sibbertsen
  Inhalt:	Prof. Gassebner Panel Data recapture (Fixed effects, random effects, etc.) Diff-in-Diff Bartik-type instruments (shift-share) Regression discontinuity design (RDD) Prof. Prokopczuk Kalman Filter Bayesian Statistics Markov Chain Monte Carlo (MCMC) Bootstrapping Prof. Sibbersten Dependence The Law of Large Numbers The Central Limit Theorem The Functional Central Limit Theorem
  Bemerkungen:	Interested PhD stdutens should register via email by 15 October 2025 at sekretariat@fcm.uni-hannover.de. Please include your matriculation number. The course will be held in blocks.

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

• 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

Kompetenzbereich Statistik

• Schließende Statistik (270158)

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

• 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.

Kompetenzbereich (Area) Empirical Economics and Econometrics

• Statistical Programming (373005)

  	Termine:	Lehrpersonen:
  	Mo. 14:30 - 16:00 | I-233	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.

• Nonparametric Statistical Methods (373010)

  	Termine:	Lehrpersonen:
  	Mi. 14:30 - 16:00 | 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:
  	Mo. 12:45 - 14:15 | I-063	Fitter
  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-063	Yu
  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-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:
  	Di. 14:30 - 16:00 | I-442	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.

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

  	Termine:	Lehrpersonen:
  	Di. 16:15 - 17:45 | I-401	Blaufus, 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.

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

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

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

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.

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.

• 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