ESF:MPE_APIS Applied id. strategies - Course Information
MPE_APIS Applied identification strategies
Faculty of Economics and AdministrationSpring 2025
- Extent and Intensity
- 2/2/0. 8 credit(s). Type of Completion: zk (examination).
In-person direct teaching - Teacher(s)
- Ing. Michal Kvasnička, Ph.D. (lecturer)
doc. Ing. Štěpán Mikula, Ph.D. (lecturer)
Mgr. Hana Fitzová, Ph.D. (assistant) - Guaranteed by
- doc. Ing. Štěpán Mikula, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration - Prerequisites
- The application of identification strategies requires the ability to use basic econometric analysis and work with data.
The course uses the open-source statistical software R (https://cran.r-project.org/) and IDE RStudio (https://www.rstudio.com/). The basic knowledge of R is a necessary prerequisite for the course. Students should have a basic knowledge of basic data structures (vector, matrix, data.frame/tibble), and regression analysis (formulas and estimation functions such as lm()) in R. Any of the courses MPE_AVED, MPE_DAAR, MPE_DAR2, or MPM_VSVS should provide sufficient background.
Some basic knowledge of econometrics or related fields (biostatistics, statistics) is also required. Students should have a basic knowledge of the OLS estimator and hypothesis testing. Any course of econometrics should provide sufficient background. - Course Enrolment Limitations
- The course is offered to students of any study field.
- Course objectives
- This course provides a set of statistical tools and research designs that allow for the identification of causal effects in empirical research in applied microeconomics or in the evaluation of public policies. We will discuss various methods in the context of analysis of labor markets, health care, education, among others. Through interactive seminars, students learn to apply theoretical designs to real-life data in statistical software R.
- Learning outcomes
- By the end of the course, students will be able to:
understand the importance of experiment in causal inference;
understand key concepts of identification strategies and their use in causal inference;
apply identification strategies in the analysis of observational data;
understand, articulate and critically discuss the need, possibilities, and methods of evaluation of public policies. - Syllabus
- The problem of policy evaluation (selection bias)
- Causal inference and counterfactuals (Rubin causal model)
- Randomized Assignment (experiments)
- Regression analysis
- Instrumental variables
- Regression discontinuity design
- Difference-in-differences
- Matching
- Literature
- required literature
- HUNTINGTON-KLEIN, Nick. The Effect : an introduction to research design and causality. First edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2022, xxvi, 619. ISBN 9781032127453. info
- recommended literature
- ANGRIST, Joshua David and Jörn-Steffen PISCHKE. Mostly harmless econometrics : an empiricist's companion. Princeton: Princeton University Press, 2009, xiii, 373. ISBN 9780691120355. URL info
- CUNNINGHAM, Scott. Causal inference: The mixtape. Yale University Press, 2021. URL info
- GERTLER, Paul, Sebastian Wilde MARTINEZ, Patrick PREMAND, Laura RAWLINGS and Christel VERMEERSCH. Impact evaluation in practice. Second edition. Washington: World bank group, 2016, xxviii, 33. ISBN 9781464807794. info
- Teaching methods
- The general concepts will be presented via lectures and case studies. The lab work with R will help students to master the practical application of identification strategies.
- Assessment methods
- Regular and active participation in seminars (30%)
Final written exam (70%) with the minimum requirement of 60% points
Evaluation:
A: (88; 100]
B: (81; 88]
C: (74; 81]
D: (67; 74]
E: (60; 67]
F: [0, 60]
The course can be completed in case of a stay abroad within Erasmus or a similar programme. In the case the evaluation is based solely on the final exam that can be taken upon arrival. However, teachers should be notified before the beginning of the semester. - Language of instruction
- Czech
- Further Comments
- The course is taught annually.
The course is taught: every week.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/econ/spring2025/MPE_APIS