• Universität Halle
  • IWH Innenhof
  • Universität Jena
  • Ifo Institut Dresden
  • Universität Leipzig
  • Albertina
  • TU Dresden
  • Universität Madgeburg
  • IWH_Tor


Lecturer: several lecturers
Begin: 10.10.2022
Time: Mondays (Week 2 and 3 Tuesday) 09:30–11:00 and 11:30–13:00
Work load: 150 hours / 6 ECTS
Lecture: Bi-weekly, 15 x 90 minutes / in person at IWH
Venue: Halle Institute for Economic Research (IWH) – Member of the Leibniz Association, Kleine Maerkerstrasse 8, 06108 Halle (Saale), conference room (ground floor). In case of tightened COVID-19 regulations, parts of the course may take place online via Zoom.
Registration: until September 30, 2022 via email: cgde@iwh-halle.de.

The course is designed for at most 25 participant.

Announcement: pdf

I. Introduction (Day 1)
1. Review of Linear Models and Asymptotic Theory
Date: 10.10.2022
Time: 09:30–11:30
Lecturer: Dr André Diegmann, IWH

II. Multivariate Kernel Regression and Finite-Sample Inference (Day 2 and Day 3)
2. Introduction to Nonparametric Models
Date: 25.10.2022 (Tuesday)
Lecturer: Jordan Adamson, PhD, University of Leipzig

3. Introduction to Nonparametric Inference
Date: 08.11.2022 (Tuesday)
Lecturer: Jordan Adamson, PhD, University of Leipzig

III. Causal Inference (Day 4 and Day 5)
4. Instrumental Variables
5. Regression Discontinuity
Date: 21.11.2022
Lecturer: Dr Matthias Mertens, IWH

6. Matching
Date: 05.12.2022
Time: 09:30–11:00 ( via Zoom)
Lecturer: Prof Xiang Li, PhD, IWH and Martin Luther University Halle-Wittenberg

7. Differences-in-Differences
Date: 09.12.2022 (via Zoom)
Time: 13:30–15:00
Lecturer: Prof Dr Felix Noth, IWH and Otto von Guericke University Magdeburg

V. Time Series (Day 6 and Day 7)
8. Time Series I
Date: 19.12.2022
Lecturer: Prof Dr Malte Rieth, Martin Luther University Halle-Wittenberg

9.  Time Series II
Date: 09.01.2023
Lecturer: Prof Dr Malte Rieth, Martin Luther University Halle-Wittenberg

VI. Special Topics (Day 8)
10.  Empirical Methods in Lab and Field Experiments
Date: 23.01.2023
Time: 09:30–11:00
Lecturer: Prof Dr Sabrina Jeworrek, IWH and Otto von Guericke University Magdeburg

11.  Machine Learning Methods for Economics and Finance
Date: 23.01.2023
Time: 11:30–13:00
Lecturer: Prof Dr Melina Ludolph, IWH and Otto von Guericke University Magdeburg

Problem sets
There will be eight assignments throughout the term. At the end of each day, the lecturer will post assignments, which are due on the day before the next lecture (11.59 pm). 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.

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 lecture-specific literature will be announced separately.