Syllabus “Panel Data Analysis with Survey Data”

Lecturers: Prof. Jan Marcus (Email: ls-angewandte-statistik@wiwiss.fu-berlin.de ) and Lukas Fink (Email: l.fink@fu-berlin.de )

Organization:

• Course language: English
• Lecture: Weekly meetings, Monday 14:15-15:45 in room HBF KII and Webex (Start: April 13)
• Tutorial: Roughly bi-weekly meetings, Thursday 10:15-11:45 in room HFB KII / seminar room 10 UG and Webex (Start April 23)

Requirements, examination, and credits:

• Requirements: Basic knowledge of econometrics equivalent to the module “Introduction to Econometrics”
• Examination: Grading will be based on a written exam (120 minutes) for which bonus points can be earned by submitting a midterm assignment
• Credits: 6 ECTS

Content:
The course offers an overview of panel data analysis using survey data. It begins by discussing the benefits and challenges of collecting repeated measurements of the same units, also known as panel data. The course then proceeds to introduce fixed and random effects estimators, as well as their underlying assumptions. An exploration of the issue of missing data in panel data analysis follows. The second half of the course is dedicated to the topic of causal analysis using panel data. Students will be presented with the classic difference-in-differences approach alongside more recent extensions, such as the method of synthetic controls and the case of staggered treatment timings. To complement the lectures, students will participate in hands-on sessions to learn how to implement the discussed panel data methods and estimators in statistical software.

Structure:
Block A: Traditional panel data analysis
1. Introduction
2. Frist difference
3. Fixed effects estimation
4. Random effects estimation
5. Panel data analysis with binary outcomes
6. Missing data

Block B: Causality and panel data
1. The difference-in-differences approach for panel data
2. The synthetic control methods
3. The new Diff-in-Diff: Difference-in-differences with staggered treatment timing

Literature:
Angrist, J. and Pischke, J. (2009) Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, New Jersey: Princeton University Press.
Angrist, J. and Pischke, J. (2015) Mastering ’metrics: The path from cause to effect. Princeton: Princeton University Press.
Backer, A., Larcker, D. and Wang, C. (2022). ’How much should we trust staggered difference-in-differences estimates?’ Journal of Financial Economics, 144(2), p.370-395.
Cameron, A. C. and Trivedi P. K. (2022) Microeconometrics using Stata: Methods and Applications. 2nd Edition. College Station, TX: Stata press
Cunningham, S. (2021) Causal inference: The mixtape. Yale: Yale University Press. https://mixtape.scunning.com/.
De Chaisemartin and D’Hautltfoeuille (2023) Difference-in-Differences for Simple and Complex Natural Experiments: Available at SSRN: https://ssrn.com/abstract=4487202 or http://dx.doi.org/10.2139/ssrn.4487202
Wooldridge, J. M. (2013) Introductory Econometrics: A Modern Approach. 5th Edition, Mason (OH): South-Western Cengage Learning.