Prof. Dr. Dirk Sliwka and Jesper Armouti-Hansen
- Sessions take place on Mondays (14:00-15:30, 16:00-17:30) and Tuesdays (10:00-11:30) in the first term of the semester.
- Please note that all sessions will take place in Zoom (See information on here).
- Exam dates:
- 1st: 18.12.2021 (10:00 - 11:00)
- Mock exam
Note: content will become available sequentially as the time of the session approaches.
- Part 0: Introduction to Python
- Part 1: Survey Data and the Reliability of Scales
- Part 2: Regressions
- Part 3: Statistical Tests
- Part 4: Regression and Causality
- Part 5: Panel Data
- Part 6: Predictions and Machine Learning
- Main literature
- Mostly Harmless Econometrics [Angrist, Pischke]
- An Introduction to Statistical Learning with Applications in R [James, Witten, Hastie, Tibshirani]
- Additional literature:
- Mastering Metrics: The Path from Cause to Effect [Angrist, Pischke]
- Introductory Econometrics: A Modern Approach [Wooldridge]
- The Elements of Statistical Learning - Data Mining, Inference, and Prediction [Hastie, Tibshirani, Friedman]
- Hands-On Machine Learning with Scikit-Learn & Tensorflow [Géron]
- Python for Data Analysi [McKinney]
- Applied Predictive Modeling [Kuhn, Johnson]
- Andrea Ichino's lecture slides (for some links to standard econometrics courses)