KU.Campus

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: