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Detailinformationen zum Modul
Modulbezeichnung:
Econometrics
Modulbezeichnung (englisch):
Econometrics
Modulnummer:
82-021-BE02-H-0218
Niveau:
Bachelor (UNI)
Geberstudiengang:
Typ:
Modul
Federführende Fakultät/Sprachenzentrum:
Wirtschaftswissenschaftliche Fakultät
Modulverantwortliche/r:
Danzer, Alexander
Prüfende:
Leistungspunkte (ECTS-Punkte):
5
Kompetenzen
:
Students of the course acquire detailed knowledge about standard (micro-)econometric techniques.
They are able to understand the theoretical concept and derivation behind econometric estimators and
have developed an intuitive understanding of their mechanics. They are able to assess and test the most
important econometric pitfalls related to these estimators.
Students have developed reflected views on the distinction between correlation and causation.
Students learn data handling with real world examples, especially in the field of public policy. They
acquire skills to implement simple econometric techniques with real world data in the computer lab.
Inhalte/Themen
:
Introduction
The linear regression model with one regressor
Estimation using OLS
Goodness-of-fit
Formal derivation of the OLS estimator
Hypothesis thesting and confidence intervals
Binary explanatory variables
Heteroskedasticity and homoscedasticity
The Gauss-Markov-Theorem
Multivariate linear regression models
The regression model and the OLS estimator
Properties of the OLS estimator
Multicollinearity
Model specification
Randomised experiment and “natural” experiments
Randomised experiments and their practical implementation
Estimation methods
Example: The Tennessee “STAR-Project”
Natural experiments
Example: Impact of minimum wages
Binary dependent variables
Non-continuous dependent variables; binary dependent variables
The linear probability model
Non-linear models: Probit and Logit
Panel data models
Panel types and organization of data
Fixed effects
Consistency and efficiency
Random effects
Instrumental variable models
The IV estimator
Two-stage least squares
Testing the IV assumptions; how can (good) instruments be found?
The simultaneity problem
Measurement error
Heterogeneous populations
Formale Voraussetzungen für die Teilnahme
:
Empfohlene Voraussetzungen
:
Mathematics, Statistics
Lehr- und Lernformen/Lehrveranstaltungstypen:
Lectures
Practical implementation in the CIP Pool
Voraussetzungen für die Vergabe von ECTS-Punkten
:
The assessment is based on a final 60-mins exam
Zeitaufwand/Verteilung der ECTS-Punkte innerhalb des Moduls
:
25 h = Time of attendance lecture
30 h = Preparation and postprocessing lecture
25 h = Time of attendance tutorial
40 h = Preparation and postprocessing tutorial
40 h = Exam preparation
150 h = Total workload
Modulnote
:
Exam 100%
Lehr- und Lernmethode
:
Polyvalenz mit anderen Studiengängen/Hinweise zur Zugänglichkeit
:
Turnus des Angebots:
SS
Beteiligte Fachgebiete:
Bemerkung: