Module 0: Formalities

Econometrics I

Max Heinze (mheinze@wu.ac.at)

Department of Economics, WU Vienna

Sannah Tijani (stijani@wu.ac.at)

Department of Economics, WU Vienna

 

 

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

Schedule

Date Time Location Exam
Wed, 04 March 14:00–16:00 TC.4.04
Wed, 11 March 14:00–16:00 TC.4.04
Wed, 18 March 14:00–16:00 TC.4.04
Wed, 25 March 14:00–16:00 TC.4.04
Easter Break
Wed, 08 April 14:00–16:00 TC.4.04
Wed, 15 April 14:00–15:30 TC.5.15 Midterm (40%)
Wed, 22 April 14:00–16:00 TC.4.04
Wed, 29 April 14:00–16:00 TC.4.04
Wed, 06 May 14:00–16:00 TC.4.04
Course Break
Wed, 27 May 14:00–16:00 TC.4.04
Wed, 03 June 14:00–16:00 TC.4.04
Wed, 10 June 14:00–16:00 TC.3.03 Final Exam (40%)

Content Plan

Date Time Location Topics
Wed, 04 March 14:00–16:00 TC.4.04 Module 0: Formalities; Module 1: Introduction (Wooldridge Ch. 1)
Wed, 11 March 14:00–16:00 TC.4.04 Module 2: Simple Linear Regression (Wooldridge Ch. 2)
Wed, 18 March 14:00–16:00 TC.4.04 Module 2: Simple Linear Regression
Wed, 25 March 14:00–16:00 TC.4.04 Module 2: Simple Linear Regression
Easter Break
Wed, 08 April 14:00–16:00 TC.4.04 Module 3: Multiple Linear Regression (Wooldridge Ch. 3)
Wed, 15 April 14:00–15:30 TC.5.15 Midterm (40%)
Wed, 22 April 14:00–16:00 TC.4.04 Module 3: Multiple Linear Regression
Wed, 29 April 14:00–16:00 TC.4.04 Module 4: Testing and Inference (Wooldridge Ch. 4 and 5)
Wed, 06 May 14:00–16:00 TC.4.04 Module 4: Testing and Inference
Course Break
Wed, 27 May 14:00–16:00 TC.4.04 Module 5: More on Multiple Regression (Wooldridge Ch. 2.4, 6.1, 6.2, 7)
Wed, 03 June 14:00–16:00 TC.4.04 Module 6: Heteroskedasticity (Wooldridge Ch. 8)
Wed, 10 June 14:00–16:00 TC.3.03 Final Exam (40%)

 

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

Components

  • 10% Participation
    • You can get two points for asking a question or making a comment
    • You have to claim them after the session
    • Participation points are capped at 6 before the midterm
  • 10% Assignment
    • Handled by our Teaching Assistant Klara
  • 40% Midterm Exam
    • Wed, April 15
    • Simple and Multiple Linear Regression (everything we have covered until then)
    • 100 minutes
  • 40% Final Exam
    • Wed, June 10
    • Entire course content
    • 100 minutes

Grading Scheme

  • The components are weighted as outlined above. The final grade is then determined by the following scale:
Grade from to
1 Very Good 87.5 %
2 Good 75.0 % < 87.5 %
3 Satisfactory 62.5 % < 75.0 %
4 Sufficient 50.0 % < 62.5 %
5 Insufficient < 50.0 %
  • No rounding is applied.

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

 

Wooldridge (2020): Introductory Econometrics

Type of Literature

  • Primary course textbook
  • Scope of material defined by the lecture slides

Availability

  • Available at the WU library, PDFs of older editions can be found online

Edition

  • We use the 7th edition, but older editions can also be used (mostly differ in page numbers, etc.)

Stock & Watson (2019): Introduction to Econometrics

Type of Literature

  • Supplementary textbook

Availability

  • Online access via WU library catalog

Angrist & Pischke (2008): Mostly Harmless Econometrics

Type of Literature

  • Supplementary literature on advanced topics
  • Introductory chapters can also be used as supplementary reading for Econometrics I

Availability

  • Available at the WU library

Hanck et al. (2024): Intro to Econometrics with R

Type of Literature

  • Online textbook
  • Useful supplementary material especially for working with R

Availability

Cunningham (2021): Causal Inference. The Mixtape

Type of Literature

  • Available in print and online
  • Supplementary reading for advanced topics
  • Introductory chapters also suitable as supplementary reading for Econometrics I

Availability

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

 

 

R, Stata, Python

  • The focus of this course is on theoretical foundations, but we will also discuss how to apply concepts using real data.
  • You will also work with data in the homework assignment.
  • You are generally free to choose which software you use to complete the tasks (results should be more or less the same):
    • R is widely used, open source, and free. Code examples in this course are provided in R.
    • Stata is proprietary but preferred by some economists.
    • Python
    • Eviews
    • Julia
    • Microsoft Excel (just don’t)