Lecturer: several lecturers
Begin: 21.10.2024
Work load: 150 hours / 6 ECTS
Lecture: Bi-weekly, 16 x 90 minutes / in person at IWH
Time: * 09:30–11:30 // ** 09:30–11:00 and 11:30–13:00
Problem sets
There will be eight assignments throughout the term. At the end of each day, the lecturer will post assignments, which are due within two weeks time. In order to complete the course, six problem sets (at least one from every block indicated by Roman numbers) will have to be successfully passed.
I. Introduction (Day 1)
1. Review of Linear Models and Asymptotic Theory
Date: 21.10.2024* (Leipziger Straße 100, Conference Room, 3rd floor)
Lecturer: Dr André Diegmann, IWH
II. Non-linear and Panel Data Models (Day 2 and Day 3)
2. Panel Data Models
3. Maximum-likelihood Estimation
Date: 04.11.2024**
Lecturer: Professor Dr Julia Schaumburg, Vrije Universiteit Amsterdam
4. Models for Binary and Categorial Outcomes
5. Models for Limited Dependent Variables
Date: 08.11.2024**
Lecturer: Professor Dr Julia Schaumburg, Vrije Universiteit Amsterdam
III. Causal Inference (Day 4 and Day 5)
6. Instrumental Variables
7. Differences-in-Differences
Date: 11.11.2024**
Lecturer: Professor Dr Felix Noth, IWH and Otto von Guericke University Magdeburg
8. Matching
Date: 25.11.2024 (Leipziger Straße 100, Conference Room, 3rd floor)
Time: 09:30–11:00
Lecturer: Professor Xiang Li, PhD, IWH and Martin Luther University Halle-Wittenberg
9. Regression Discontinuity
Date: 25.11.2024 (Leipziger Straße 100, Conference Room, 3rd floor)
Time: 11:30–13:00
Lecturer: Professor Dr Felix Noth, IWH and Otto von Guericke University Magdeburg
IV. Time Series (Day 6 and Day 7)
10. Time Series I
Date: 13.12.2024**
Lecturer: Professor Dr Malte Rieth, Martin Luther University Halle-Wittenberg
11. Time Series II
Date: 07.01.2025** (Leipziger Straße 100, Conference Room, 3rd floor)
Lecturer: Professor Dr Malte Rieth, Martin Luther University Halle-Wittenberg
V. Special Topics (Day 8)
12. Empirical Methods in Lab and Field Experiments
Date: 20.01.2025
Time: 09:30–11:00
Lecturer: Professor Dr Sabrina Jeworrek, IWH and Otto von Guericke University Magdeburg
13. Machine Learning Methods for Economics and Finance
Date: 20.01.2025
Time: 11:30–13:00
Lecturer: Professor Dr Fabian Wöbbeking, IWH and Martin Luther University Halle-Wittenberg
Selected Literature
Althey, S.; Imbens, G. W. (2019): Machine Learning Methods that Economists Should Know About. Annual Review of Economics 11, 685-729.
Angrist, J. D.; Pischke, J.-S. (2015): Mastering Metrics. Princeton University Press.
Angrist, J. D.; Pischke, J.-S. (2009): Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
Cameron, A.C.; Trivedi, P.K. (2005): Microeconometrics, Methods and Applications, Cambridge University Press.
Gelman, A.; Carlin, J. B.; Stern, H. S.; Dunson, D. B.; Vehtari, A.; Rubin, D. B. (2013): Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Press.
Greene, W.H. (2017): Econometric Analysis, 8th edition, Pearson.
Imbens, G. W.; Rubin, D. B. (2015): Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
Kilian, L.; Lütkepohl, H. (2017): Structural Vector Autoregressive Analysis, Cambridge University Press, 2017.
McElreath, R. (2020): Statistical Rethinking. A Bayesian Course with Examples in R and Stan. Chapman & Hall/CRC Press.
Winkelmann, R.; Boes, S. (2006): Analysis of Microdata. Springer.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
Additional literature to prepare for every dates will be announced.