Syllabus

Title
1969 Econometrics II
Instructors
Nikolas Kuschnig, MSc (WU)
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/15/21 to 09/22/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Thursday 10/07/21 02:00 PM - 04:00 PM D5.1.004
Thursday 10/14/21 02:00 PM - 04:00 PM D4.0.019
Thursday 10/21/21 02:00 PM - 04:00 PM D5.1.004
Thursday 10/28/21 02:00 PM - 04:00 PM D5.1.004
Thursday 11/04/21 02:00 PM - 04:00 PM D5.1.004
Thursday 11/11/21 02:00 PM - 04:00 PM D5.1.004
Thursday 11/18/21 02:00 PM - 04:00 PM D5.1.004
Thursday 11/25/21 02:00 PM - 04:00 PM Online-Einheit
Thursday 12/02/21 02:00 PM - 04:00 PM Online-Einheit
Thursday 12/09/21 02:00 PM - 04:00 PM Online-Einheit
Thursday 12/16/21 02:00 PM - 04:00 PM Online-Einheit
Thursday 01/13/22 02:00 PM - 04:00 PM D5.1.004
Thursday 01/20/22 02:00 PM - 04:00 PM D5.1.004
Thursday 01/27/22 02:00 PM - 04:00 PM TC.4.05

Contents

This course covers advanced subjects in econometrics, focusing on causal inference and model building. We will cover common problems of regression analysis and potential remedies. Applied examples and assignments will be laid out to use the R language.

The following topics will be covered in this course:

  • Endogeneity (omitted variables, simultaneity, data errors)
  • Instrumental variables (two-stage least squares, simultaneous equations)
  • Causal effects (prerequisites, model building, diff-in-diff)

Prior knowledge of the following topics is expected:

  • Multivariate regression (application, interpretation)
  • Regression properties (least squares estimation, classical assumptions, estimator properties, Gauss-Markov theorem)
  • Regression inference (hypothesis testing, confidence intervals, model selection)
  • Assumption failures and remedies (heteroskedasticity, serial correlation, multicollinearity)
  • Functional forms (dummy variables, interaction terms, log-transformations)

These are covered in Econometrics I -- it is assumed that you have a solid understanding of them. In addition, it would be beneficial to have working knowledge of R, e.g. from the Statistics with R course.

 

Learning outcomes

After this course you will be equipped to conduct advanced econometric analyses. You will be aware of common pitfalls and how they may be dealt with. That includes a solid understanding of causal inference -- under which conditions it is possible and how these may be induced. You will be able to independently apply your knowledge using R and critically review applied research.

 

Attendance requirements

Attendance (synchronous or asynchronous) is compulsory. Exceptions are only possible in exceptional circumstances on a per case basis.

Teaching/learning method(s)

The course material will be presented in the form of slides, with accompanying assignments for better comprehension throughout. Assignments are designed to use R, will be worked on in groups, and need to be handed in in written form.
The first half of the course will focus more on econometric theory, while the second half will lean towards more practical issues. The midterm in December will cover these theoretical underpinnings. The final exam in the end of January will cover all of the course material.

 

Assessment

Assessment will be based on three components:

  • 40% group assignments
  • 20% midterm exam
  • 40% final exam

The grading scheme is as follows:

  1. [88, 100]
  2. [75, 88)
  3. [62, 75)
  4. [50, 62)
  5. [0, 50)

 

Prerequisites for participation and waiting lists

Successful completion of Econometrics I is highly recommended. Prior knowledge in statistics, linear algebra, and R is recommended.

Participants on the waiting list may receive a spot in the course automatically, which usually happens early on. In addition, some spots may become available in the first session -- if you want to try to get a spot this way please notify the lecturer the week before.

 

Readings

1 Author: Hanck et al.
Title:

Introduction to Econometrics with R


Remarks: https://www.econometrics-with-r.org/
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
2 Author: Wooldrige
Title:

Introductory Economics


Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
3 Author: Hackl
Title:

Einführung in die Ökonometrie


Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
4 Author: Stock and Watson
Title:

Introduction to Econometrics


Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book

Availability of lecturer(s)

Other

For a quick start to using R, see this short introduction. Many of this course's topics are covered in Introduction to Econometrics with R.

For more general and advanced material, see R for Data Science, and Advanced R.

Last edited: 2021-09-06



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