Syllabus

Title
0407 Econometrics II
Instructors
Ass.Prof. PD Michael Pfarrhofer, Ph.D.
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/12/24 to 09/17/24
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Thursday 10/17/24 08:00 AM - 10:00 AM TC.4.13
Thursday 10/24/24 08:00 AM - 10:00 AM TC.4.13
Thursday 11/07/24 08:00 AM - 10:00 AM TC.4.13
Thursday 11/14/24 08:00 AM - 10:00 AM TC.4.13
Thursday 11/21/24 08:00 AM - 10:00 AM TC.4.13
Thursday 11/28/24 08:00 AM - 10:00 AM TC.4.13
Thursday 12/05/24 08:00 AM - 10:00 AM TC.4.13
Thursday 12/12/24 08:00 AM - 10:00 AM TC.4.13
Thursday 12/19/24 08:00 AM - 10:00 AM TC.4.13
Thursday 01/09/25 08:00 AM - 10:00 AM TC.4.04
Thursday 01/16/25 08:00 AM - 10:00 AM TC.4.13
Thursday 01/23/25 08:00 AM - 10:00 AM TC.4.13
Contents

This course covers topics in applied econometrics and consists of three main parts:

  1. Review of the linear regression model and extensions to be used when the usual assumptions are likely not fulfilled (e.g., in the presence of endogeneity). This also includes an introduction to maximum likelihood estimation.
  2. Time series analysis and associated concepts such as stationarity and non-stationarity; ARMA and ARIMA models are introduced alongside applications to predictions and forecasting.
  3. Limited dependent variable models (e.g., logit and probit models).

Theoretical input will be complemented with real-world applications in the statistical software R.

Learning outcomes

The course provides an introduction to analyzing economic data using econometric methods that go beyond the multiple regression model discussed in Econometrics I. After completing the course, students are able to understand and evaluate empirical studies that use the methods outlined in "Contents." In addition, students are able to perform their own statistical analyses which make use of these methods.

Attendance requirements

Attendance is mandatory for this course, one absence will be tolerated.

Teaching/learning method(s)

Course materials will be made available to participants in the form of slides and computer code. The concepts we discuss theoretically are illustrated empirically using the statistical software R.

To gain experience in working with empirical data and to illustrate real-world applications, students will work in groups on homeworks and on a small empirical project. Solutions must be handed in as written reports.

 

Assessment
The assessment is based on:
  1. Exam
  2. Homework
  3. Project report

To pass the course, a positive score on each of these components is necessary (i.e., at least 50 percent of the respective total points).

Grading scheme:

  • Excellent (1): [89, 100] points
  • Good (2): [78, 89) points
  • Satisfactory (3): [60, 78) points
  • Sufficient (4): [50, 60) points
  • Fail (5): [0, 50) points

 

Prerequisites for participation and waiting lists
  • Automatic deregistration from the course in the event of an unexcused no show in the first lecture (participants will be added from the waiting list if applicable).
  • Non-assessment in the event of unexcused no shows if no partial performance was provided.
  • Negative assessment for unexcused no show if at least one partial performance has already been provided (e.g., case study).
Readings

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Recommended previous knowledge and skills

Successful completion of Econometrics I.

Last edited: 2024-08-21



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