Module 0: Formalities

Applied Econometrics

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

Content Plan

Date Time Location Topics
Thu, 05.03.2026 14:00–16:00 TC.3.11 Module 1: Time Series and Autocorrelation
Thu, 12.03.2026 14:00–16:00 TC.3.11 Module 1: Time Series and Autocorrelation
Thu, 19.03.2026 14:00–16:00 TC.3.11 Module 1: Time Series and Autocorrelation
Thu, 26.03.2026 14:00–16:00 TC.3.11 Module 1: Time Series and Autocorrelation
Easter Break
Thu, 09.04.2026 14:00–16:00 TC.3.11 Module 2: Panel Data and Further Issues
Break
Thu, 23.04.2026 14:00–16:00 TC.3.11 Module 2: Panel Data and Further Issues
Thu, 30.04.2026 14:00–16:00 TC.3.11 Module 2: Panel Data and Further Issues
Thu, 07.05.2026 14:00–16:00 TC.3.11 Module 2: Panel Data and Further Issues
Break
Thu, 21.05.2026 14:00–16:00 TC.3.21 Final Exam
Thu, 28.05.2026 14:00–17:00 TC.3.11 Presentations
Thu, 11.06.2026 14:00–17:00 TC.3.11 Presentations

 

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

Components (1)

  • Final Exam
    • 40%
    • Cheat Sheet allowed (details to follow)
  • Project Submission
    • 30%
    • To be conducted alone or in pairs
    • Goal: Apply econometric methods by conducting a research project from start to finish for the first time
    • Can continue an Econometrics II project, if you did one
    • Can serve as inspiration for the bachelor thesis

Components (2)

  • Project Presentation
    • 20%
    • Presentation of your project submission, thus in the same groups
    • About 15 minutes, with questions allowed throughout and no separate discussion (details to follow)
  • Active Participation
    • 10%
    • To get full points, you must actively participate in at least 4 lectures (lectures end before the exam) and pose a question or comment on 1 presentation.
    • Attendance ≠ Participation

Vote on Project Submission Deadline




Grading Scheme

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

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

 

Stock & Watson (2019): Introduction to Econometrics

Type of Literature

  • Main course literature for some topics

Availability

  • Online access via WU library catalog

Cunningham (2021): Causal Inference. The Mixtape

Type of Literature

  • Main course literature for some topics

Availability

Wooldridge (2020): Introductory Econometrics

Type of Literature

  • Good for review of Econometrics I

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.)

Angrist & Pischke (2008): Mostly Harmless Econometrics

Type of Literature

  • Supplementary literature on advanced topics

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

James et al. (2021): An Introduction to Statistical Learning

Type of Literature

  • Supplementary literature

Availability

  • Available at the WU Library

James et al. (2021): An Introduction to Statistical Learning

Type of Literature

  • Supplementary literature, good for reviewing Econometrics II

Availability

  • Available at the WU Library

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

 

 

R, Stata, Python

  • For your project, you will need to apply what we have learned using real data.
  • You might also work with data in the future.
  • You are generally free to choose which software you use to complete the tasks, as you will be in real life (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)