Vorlesung: 35621 Computational Statistics - Regression in R - Details

Vorlesung: 35621 Computational Statistics - Regression in R - Details

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Veranstaltungsname Vorlesung: 35621 Computational Statistics - Regression in R
Untertitel
Veranstaltungsnummer 35621
Semester SoSe 21
Aktuelle Anzahl der Teilnehmenden 152
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 Dienstag, 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

Räume und Zeiten

(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.