Allgemeine Informationen

Untertitel
Veranstaltungsnummer	35550
Semester	SoSe 23
Aktuelle Anzahl der Teilnehmenden	29
Heimat-Einrichtung	Lehreinheit für Computergestützte Statistik und Mathematik
beteiligte Einrichtungen	Lehrstuhl für Statistik und Data Analytics
Veranstaltungstyp	Vorlesung in der Kategorie Lehre (mit Prüfung)
Erster Termin	Mi., 19.04.2023 08:00 - 10:00 Uhr, Ort: (ZMK) SR 004
Art/Form
Voraussetzungen	An understanding of introductory statistics including inferential methods and regression analysis and test methods on bachelor level. Prior basic knowledge of statistic software R is an advantage but not necessary.
Lernorganisation	This lecture is organized in a set of lectures and tutorials (Übungen). In the tutorials students solve problem sets corresponding to the topics of the lecture, some of the tutorials are hands-on using the open-source statistical software R. Students are explicitly invited to play an active role in lectures and tutorials through questions and input for discussions. Additionally, students are invited to indicate those parts of the course for which they need additional training. Readings are essential to prepare the class and the exam.
Leistungsnachweis	Portfolio, consisting of two parts: • Part 1 (1/3): Short presentation of (a part of) a scientific paper or an application. • Part 2 (2/3): Performance assessment at home (Häusliche Leistungsfeststellung) or oral exam at the end of the semester.
SWS	2
Literatur	Among others and depending on the selection of topics: Angrist, J.D. & Pischke J.-S. (2009); Mostly Harmless Econometrics, Princeton. Cameron, C.A. & Trivedi, P.K. (2005), Microeconometrics: Methods & Applications, Cambridge. Franses, P.H., van Dijk, D. & A. Opschoor (2014), Time Series Models for Business and Economic Forecasting, Cambridge. Kleiber, C. & Zeileis, A. (2008), Applied Econometrics with R, Springer. Verbeek, M.. (2017), A Guide to Modern Econometrics, 5e, Wiley
Turnus	Usually every summer term
Workload	Lecture 2 SWS (28 h Contact hours and 28 h Self-study) Tutorial 2 SWS (28 h Contact hours, 28 h Self-study) We are calculating with 15 semester weeks (14 lecture + 1 examination week). Each SWS is included in the calculation with 60 minutes.
ECTS-Punkte	5

Lehrende

Veranstaltungsort / Veranstaltungszeiten

(ZMK) SR 004

Mi. 08:00 - 10:00 (13x)

Studienbereiche

Modulzuordnungen

Kommentar/Beschreibung

In this course we study a selection (usually divided in three to four blocks) of important reseach
methods and techniques in applied econometrics. Topics included are:
Maximum-Likelihood estimation and inference (for specification tests and various fields of
microeceonometric applications), advanced applications of least squares and GMM (for modeling
heterogeneity and endogeneity in empirical practice), smoothing methods in action (such as
kernel and spline estimation techniques), robust inferential methods and their interpretation (such
as quantile regression and related techniques), machine learning methods (and their applications
in econometrics), simulation based econometric methods (such as Bootstrap, MC and Bayesian techniques).