Wirtschaftswissenschaftliche Fakultät

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Econometrics


Lecturer: C. Heiberger
Date: see timetable
Building/Room: see timetable


Abstract:

This course is an introduction to econometric methods emphazising their possible applications. These methods are frequently used in economics as well as in business administration for decision making, controlling and testing of hypotheses. This course's focus lies on linear regression models with several applications to economic and business related problems. The aim is to provide a framework to formulate and solve economic problems by using the developed methods and appropriate software. There is a supporting tutorial for this course introducing the econometric software package EViews and giving further applications of the methods tought during the lecture. As a prerequisite for the lecture, the visitor is expected to have some basic knowledge in probability theory and statistics.


Content:

  1. Cross-Sectional Data
    1. The bivariate regression model
    2. Hypothesis tests in the bivariate regression model
    3. The multivariate regression model
    4. Hypothesis tests in the multivariate regression model
    5. Model specification (omitted variable, functional form, multicollinearity, heteroscedasticity)
  2. Time series data
    1. Stochastic processes
    2. The OLS estimator
    3. Misspecification (stability, endogenous regressors, autocorrelation, non-stationarity
  3. Instrumental variable estimation


Literature:

Greene, William H., Econometric Analysis, 7th Ed., Pearson, Boston 2012

Stock, James H. and Mark Watson, Introduction to Econometrics, 3rd. Ed., Pearson, Boston

Wooldridge, Jeffrey M., Introductory Econometrics, 5th Ed., South-Western Cengage Learning, 2013


More information:

Recommended semester: Bachelor
Field of study: Bach. iBWL/iVWL: Fortgeschr. Methoden (PO 2008); Diplom iVWL/iBWL: AVWL (PO 2005); Bach. GBM Fortschr. Methoden (PO 2008), (PO 2013); Bach. Rewi VWL; Bach. VWL-Nebenf. (Phil.-Hist.)
Duration: 2 SWS
Type: V - Vorlesung
Semester: WS 2017/18