Module 0: Formalities

Econometrics II

Max Heinze (mheinze@wu.ac.at)

Department of Economics, WU Vienna

Sannah Tijani (stijani@wu.ac.at)

Department of Economics, WU Vienna

October 16, 2025

 

 

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

Content Plan

Date Time Location Topics
Thu, 16 October 14:30–16:30 TC.4.15 Module 1: Statistical Learning and the Role of Econometrics
Thu, 23 October 14:30–16:30 TC.4.15 Module 2: Causality
Thu, 30 October 14:30–16:30 TC.3.12 Module 2: Causality (Directed Acyclic Graphs)
Thu, 6 November 14:30–16:30 TC.3.12 Module 3: Threats to Causal Identification
Thu, 13 November 14:30–16:30 TC.3.12 Module 3: Threats to Causal Identification
Thu, 20 November 14:30–16:30 TC.4.15 First Partial Exam (30%)
Break
Thu, 4 December 14:30–16:30 TC.3.12 Module 4: Instrumental Variables
Thu, 11 December 14:30–16:30 TC.4.13 Module 4: Instrumental Variables
Thu, 18 December 14:30–16:30 TC.3.12 Moduel 5: Non-Linear Models
Christmas Break
Thu, 8 January 14:30–16:30 TC.4.13 Module 5: Maximum Likelihood
Thu, 15 January 14:30–16:30 TC.3.12 Module 6: More Identification Strategies
Thu, 22 January 14:30–16:30 TC.3.12 Second Partial Exam (30%)

 

 

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

Components

  • First Partial Exam
    • 30%, Modules 1–3
  • Second Partial Exam
    • 30%, Module 4–6 (Modules 1–3 can appear as base knowledge)
  • Project
    • 30%
    • To be conducted alone or in pairs
    • Goal: Deepen knowledge by applying course content
    • Can serve as inspiration for the bachelor thesis
  • Active Participation
    • 10%, to get full points you must actively participate in at least 5 lectures.
    • Attendance ≠ Participation
  • Bonus
    • Forecasting competition on kaggle

Forecasting Competition

Econometrics is also useful for prediction.

  • You can learn a lot about prediction via trial-and-error.
  • To facilitate that, there will be a voluntary forecast competition on Kaggle.

You can find the rules on Kaggle. You can join the competition via this link1: Open Kaggle

We will award bonus points on two deadlines. Bonus points are cumulative, so you can earn a maximum of 5 bonus points.

First round (4/12/2025) Second round (15/01/2026)
2 pts for places 1–3 3 pts for 1st
1 pt for places 4–10 2 pts for 2nd
1 pt for places 3-5

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

 

 

Cunningham (2021): Causal Inference. The Mixtape

Type of Literature

  • Available in print and online
  • Supplementary reading for most 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.)

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

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

  • Textbook
  • Used as base for some of Module 1

Availability

  • Available at the WU Library

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

Type of Literature

  • Textbook
  • Background reading materal for Module 2

Availability

  • Available at the WU Library

Semester Plan

Assessment Criteria

Textbooks

Statistical Software

 

 

 

R, Stata, Python

  • The focus of this course is on identification strategies, which we will need to apply using real data.
  • You might also work with data in the project.
  • 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)