Peter Haan, Hannes Ullrich, Felix Weinhardt, Maximilian Schaller
Spring Semester 2026

This course discusses central methods and current advances in applied econometrics. We provide insights into empirical strategies developed in important papers in the Labour, Public & IO literatures (now applied in many other fields), with a focus on identification. We discuss in-depth a variety of econometric frameworks and their core assumptions for causal and counterfactual analysis. We give students an understanding of why and when adding structure informed by economic theory can be important. We establish basic estimation techniques & numerical methods such as simulation and numerical integration.

Course organization
• The course takes place on Fridays (in general), 9:00 - 12:00 at DIW Berlin (Room Ostrom).
• All material can be found here: https://github.com/AppliedEconBerlin/bsoe_gc_aae_2026
• PhD: Credit points: 9 ECTS
• Master: Credit points: 6 ECTS
• First session: April 17, 2026
• Final session: July, 10, 2026
• Exam: July, 17, 2026
• Compulsory reading in bold.

• PhD Evaluation: if this course is taken for credits, the final grade will be determined by
– 4 problem sets (to be completed in groups of max. 3 participants), weighted 1/8 each,
– a final exam, weighted 1/2.
• Master Evaluation:
– 3 problem sets out of 4 (to be completed in groups of max. 3 participants),
– a final exam (weighting depends on the rules of your program)

Course objectives
• Discuss central methods and current advances in applied econometrics.
• Provide insights into empirical strategies developed in important papers in the Labour, Public & IO
literatures (now applied in many other fields), with a focus on identification.
• Discuss in-depth a variety of econometric frameworks and their core assumptions for causal and
counterfactual analysis. Give students an understanding of why and when adding structure informed
by economic theory can be important.
• Establish basic estimation techniques & numerical methods such as simulation and numerical integration.

1 Omitted Variable Bias, Assessing Models, Fisher Inference,
Stata (April 17, FW)
In this session, we will cover different topics of general interest, before turning to more specific methods.
These are (a) sometimes we are just left with the OLS. Can we learn anything from coefficient movements
and the stability of our estimates when including or excluding more controls?, (b) in this course we will not
talk about inference much but here is one powerful method for inference in experiments (that can also be
applied in non-experimental settings) that works without distributional assumptions, its Fisher inference...,
(c) for both topics we will use Stata, so this is being introduced alongside.
• Assessing endogeneity problems in OLS through coefficient movements
• Fisher inference
• Introduction to Stata software
References
Oster, Emily, (2019), Unobservable Selection and Coefficient Stability: Theory and Evidence, Journal
of Business & Economic Statistics, 37, issue 2.
Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on Observed and Unobserved Variables:
Assessing the Effectiveness of Catholic Schools. Journal of Political Economy, 113(1), 151ˆa€“184.
Alwyn Young, Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly
Significant Experimental Results, The Quarterly Journal of Economics, Volume 134, Issue 2,
May 2019.
Scott Cunningham (2018), Causal Inference: The Mixtape, Chapter “Randomization Inference”,
https://mixtape.scunning.com/04-potential_outcomes#randomization-inference.
Richard Murphy, and Gill Wyness and Felix Weinhardt (2021) Who teaches the teachers? A RCT
of peer-to-peer observation and feedback in 181 schools. The Economics of Education Review 2021,
vol. 82. https://doi.org/10.1016/j.econedurev.2021.102091

2 Panel Data/Fixed Effects (April 24, FW) - note change of room
and location only for this session: HU Hörsaal 203
• Fixed effects and first differences: identification and interpretation
• Application to panel settings and beyond
References
Raj Chetty, Adam Looney, and Kory Kroft (2009), Salience and Taxation: Theory and
Evidence, American Economic Review, 99 (4), 1145-1177.
Scott Cunningham (2018), Causal Inference: The Mixtape, Chapter “Panel Data”,
https://mixtape.scunning.com/08-panel_data.
Jens Hainmueller and Dominik Hangartner (2019), Does Direct Democracy Hurt Immigrant Minorities?
Evidence from Naturalization Decisions in Switzerland, American Journal of Political Science,
63 (3), 530-547.
Victor Lavy, Olmo Silva and Felix Weinhardt (2012) The Good, The Bad and The Average: Evidence
on Ability Peer Effects in Schools, Journal of Labor Economics, 20 (2), pp. 367-414. https://doi.
org/10.1086/663592