MPE_APIS Applied identification strategies

Faculty of Economics and Administration
Spring 2023
Extent and Intensity
2/2/0. 8 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Michal Kvasnička, Ph.D. (lecturer)
doc. Ing. Štěpán Mikula, Ph.D. (lecturer)
Isha Gupta, B.A., M.Sc., Ph.D. (seminar tutor)
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
Timetable
Mon 14:00–15:50 P403, except Mon 27. 3.
  • Timetable of Seminar Groups:
MPE_APIS/01: Tue 10:00–11:50 VT204, except Tue 28. 3., M. Kvasnička, Š. Mikula
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
  • 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
    recommended literature
  • CUNNINGHAM, Scott. Causal inference: The mixtape. Yale University Press, 2021. URL info
  • 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
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]
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2022, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2023, recent)
  • Permalink: https://is.muni.cz/course/econ/spring2023/MPE_APIS