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Event

Applied Econometrics [WS222520020]

Type

lecture (V)
Präsenz/Online gemischt

Term

WS 22/23

SWS

Language

Englisch

Appointments

Links

ILIAS

Lecturers

Prof. Dr. Fabian Krüger

Organisation

KIT-Fakultät für Wirtschaftswissenschaften

Part of

Brick Applied Econometrics | Industrial Engineering and Management (M.Sc.)
Brick Applied Econometrics | Economics Engineering (M.Sc.)
Brick Applied Econometrics | Information Systems (M.Sc.)
Brick Applied Econometrics | Information Engineering and Management (M.Sc.)
Brick Applied Econometrics | Economathematics (M.Sc.)

Literature

Angrist, J.D., and J.-S. Pischke (2009): Mostly Harmless Econometrics. Princeton University Press.

Cattaneo, M.D., N. Idrobo and R. Titiunik (2020): A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.

Hansen, B. (2022): Econometrics. Princeton University Press.

DiTraglia, F.J. (2021): Lecture Notes on Treatment Effects. Course notes, available at
https://www.treatment-effects.com/.

Appointments

26.10.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
02.11.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
09.11.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
16.11.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
23.11.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
30.11.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
07.12.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
14.12.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
21.12.2022 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
11.01.2023 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
18.01.2023 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
25.01.2023 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
01.02.2023 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
08.02.2023 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude
15.02.2023 14:00 - 15:30 - Room: 10.11 Seminarraum Hauptgebäude

Note

Content:

The course covers two econometric topics: (1) Conditional expectation and regression, and (2) Causal inference. Part (1) reviews foundations like the best linear predictor, least squares estimation, and robust covariance estimation. Part (2) introduces the potential outcomes framework for studying causal, what-if type questions such as `How does an internship affect a person's future wage?'. It then presents research strategies like randomized trials, instrumental variables, and regression discontinuity.

For each part, we discuss econometric methods and theory, empirical examples (including recent research papers), and R implementation.

Learning goal:

Students are able to assess the properties of various econometric estimators and research designs, and to implement econometric estimators using R software.

Workload:

Total workload for 4.5 CP: approx. 135 hours

Attendance: 30 hours

Independent Study: 105 hours