Modulhandbuch (Module manual)

M.184.4452 Advanced Econometrics Using R
(Advanced Econometrics Using R)

Koordinator (coordinator):	Prof. Dr. Yuanhua Feng
Ansprechpartner (contact):
Credits:	5 ECTS
Workload:	150 Std (h)
Semesterturnus (semester cycle):	SoSe
Studiensemester (study semester):	1-4
Dauer in Semestern (duration in semesters):	1

Lehrveranstaltungen (courses):
	Nummer / Name (number / title)	Art (type)	Kontaktzeit (contact time)	Selbststudium (self-study)	Status (P/WP) (status)	Gruppengröße (group size)
a)	K.184.44521 / Advanced Econometrics Using R (Vorlesung)	Vorlesung			P
b)	K.184.44522 / Advanced Econometrics Using R (Übung)	Übung			P
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Keine

Empfohlene Voraussetzungen (prerequisites):
W1471 Basic Principles of Statistics I W1472 Basic Principles of Statistics II W4479 Econometrics (or W2451 Einführung in die Ökonometrie)

Inhalte (short description):
The students will be introduced to different advanced topics of modern econometrics and their applications in empirical economic research, including nonlinear regression, nonparametric regression, robust regression, analysis of panel data as well as simultaneous equation models. Particularly, nonparametric regression and analysis of panel data will be introduced in some detail. Topics in nonparametric regression are e.g. basic concepts of nonparametric regression, the reason why this (relatively complex) technique should be used, kernel regression, local polynomial regression, asymptotic results of nonparametric regression estimators, relationship between the above two approaches, data-driven bandwidth selection as well as practical implementation of these approaches. Nonparametric density estimation may also be discussed briefly. For panel data analysis, the history, properties and importance and well known resources of panel data will be described first. One-way error component regression and two-way error regression models will then be introduced. In both cases fixed effect and random effect models will be discussed and compared. Further topics are the maximum likelihood estimation of panel data models, test of the estimated models, prediction based on the models and test of hypotheses with panel data.

Inhalte (short description):

The students will be introduced to different advanced topics of modern econometrics and their applications in empirical economic research, including nonlinear regression, nonparametric regression, robust regression, analysis of panel data as well as simultaneous equation models. Particularly, nonparametric regression and analysis of panel data will be introduced in some detail.

Topics in nonparametric regression are e.g. basic concepts of nonparametric regression, the reason why this (relatively complex) technique should be used, kernel regression, local polynomial regression, asymptotic results of nonparametric regression estimators, relationship between the above two approaches, data-driven bandwidth selection as well as practical implementation of these approaches. Nonparametric density estimation may also be discussed briefly.

For panel data analysis, the history, properties and importance and well known resources of panel data will be described first. One-way error component regression and two-way error regression models will then be introduced. In both cases fixed effect and random effect models will be discussed and compared. Further topics are the maximum likelihood estimation of panel data models, test of the estimated models, prediction based on the models and test of hypotheses with panel data.

Lernergebnisse (learning outcomes):

Fachkompetenz Wissen (professional expertise):
Studierende... know different advanced methods in econometrics and their applications, such as nonparametric regression, robust regression and panel data analysis.
Fachkompetenz Fertigkeit (practical professional and academic skills):
Studierende... can use smoothing techniques, asymptotics, data-driven data analysis, maximum likelihood, hypotheses testing, prediction, robust estimation, simultaneous equation models. know fixed and random effect regression, one-way and two-way models, the phenomenon of misspecification and optimal data analysis.
Personale Kompetenz / Sozial (individual competences / social skills):
Studierende... work in groups. present and discuss solutions to exercises.
Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
Studierende... receive training for further learning in Econometrics and Statistics. learn skills to deal with large and complex data sets.

Prüfungsleistungen (examinations)
Art der Modulprüfung (type of modul examination): Modulabschlussprüfung
	Art der Prüfung (type of examination)	Umfang (extent)	Gewichtung (weighting)
a)	Klausur	90 Min.	70.00 %
b)	Projektarbeit	10 S.	30.00 %

Studienleistung / qualifizierte Teilnahme (module participation requirements)
Nein

Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
Keine

Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist

Gewichtung für Gesamtnote (calculation of overall grade)
Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)

Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
M.Sc. International Business Studies, M.Sc. Betriebswirtschaftslehre, M.Sc. International Economics and Management, M.Sc. Management Information Systems, M.Sc. Wirtschaftsinformatik, M.Sc. Wirtschaftspädagogik, M. Ed. Wirtschaftspädagogik

Umfang QT (participation requirements):

Lernmaterialien, Literaturangaben (learning material, literature):

Teilnehmerbegrenzung (participant limit):
Keine

Sonstige Hinweise (additional information):
Unterrichtssprache: English

Sonstige Hinweise (additional information):

Unterrichtssprache:

English

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