Allgemeine Informationen

Untertitel
Veranstaltungsnummer	35621
Semester	SoSe 21
Aktuelle Anzahl der Teilnehmenden	151
erwartete Teilnehmendenanzahl	20
Heimat-Einrichtung	Lehreinheit für Computergestützte Statistik und Mathematik
beteiligte Einrichtungen	Graduiertenzentrum, Lehrstuhl für Statistik und Data Analytics
Veranstaltungstyp	Vorlesung in der Kategorie Lehre (mit Prüfung)
Erster Termin	Di., 13.04.2021 10:00 - 12:00 Uhr, Ort: (Online (Zoom))
Art/Form
Voraussetzungen	The course aims at students with a basic knowledge in statistics and complements some of the topics treated in 'Methods in Econometrics I and II'.
Lernorganisation	Guided computer tutorials; students are expected to deepen their knowledge by completing self-contained R-exercises and by presenting/explaining code snippets.
Leistungsnachweis	Final exam (60 minutes); R-skills are certified via a certificate when the final exam is passed.
SWS	2
Literatur	Kleiber, C. & A. Zeileis (2008), Applied Econometrics with R, Springer. Field, A. & Miles, J. & Field, Z. (2012), Discovering Statistics using R, SAGE. Wooldridge, J. (2013), Introductory Econometrics, 5Ed., South Western. Greene, W.H. (2012), Econometric Analysis, Pearson. Ligges, U. (2008), Programmieren mit R, Springer.
Turnus	Usually each winter term
Qualifikationsziele	The course aims at providing students with a basic understanding, which regression models to employ for certain types of variables and data structures. A further course objective is to enable students to choose between competing model specifications and to judge if a given model is (severely) misspecified.
Workload	2 SWS (30 h attendance, 45-60 h self-study)
Sonstiges	Course is taught in English.
ECTS-Punkte	3

Lehrende

Veranstaltungsort / Veranstaltungszeiten

(Online (Zoom))

Dienstag: 10:00 - 12:00, wöchentlich (13x)

Studienbereiche

Die Angaben zu den Anrechenbarkeiten an der FIM sind ohne Gewähr. Bitte beachten Sie die verbindliche Liste der Anrechenbarkeiten .

Kommentar/Beschreibung

The course focuses on estimating regression models and evaluating the estimated specifications with the statistical software R. Model evaluation procedures discussed in class range from graphical methods, classic validation techniques and tests to simulation-based approaches. The effects of variables being measured on different scales and variable transformations are discussed. Dealing with different data structures such as cross-sections, time series, and panel data is also covered in class.