DXE_EMTR Econometrics

Faculty of Economics and Administration
Autumn 2021
Extent and Intensity
24/0/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Lukáš Lafférs, PhD. (lecturer)
doc. Ing. Štěpán Mikula, Ph.D. (assistant)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Lucie Přikrylová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Thu 30. 9. 14:00–17:50 S307, Fri 1. 10. 9:00–12:50 S307, Thu 11. 11. 14:00–17:50 S307, Fri 12. 11. 9:00–12:50 S307, Thu 6. 1. 14:00–17:50 S307, Fri 7. 1. 9:00–11:00 S307, 12:00–14:00 S307
Prerequisites
Course participants should be familiar with linear algebra, probability theory, statistics and econometrics on a basic level.
Linear algebra: Simon, Carl P., and Lawrence Blume. Mathematics for economists. Vol. 7. New York: Norton, 1994. (chapters 8,9,10,11)
Probability theory and statistics: Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. Cengage learning, 2015. (Appendix B, C)
Econometrics: Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. Cengage learning, 2015. (Part I, Glossary)
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
there are 26 fields of study the course is directly associated with, display
Course objectives
The purpose of this course is to make students familiar with the wide range of econometric topics that they may find relevant throughout their PhD studies. Special emphasis is given on identification and on exploring causal mechanisms from observational data.
Learning outcomes
Successful course participant will understand most of the basic tools in the modern econometric toolbox. Participant will be able to critically assess and discuss the validity of the identification setup and empirical estimation strategy.
Syllabus
  • Regression basics: assumptions of the regression model, geometry of linear squares, confidence intervals, purpose: prediction vs explanation, correlated variables, weighted regression, transformations and model selection: bias-variance trade-off
  • Identification: The problem of identification, defining the object of interest, separating identification from estimation
  • Statistical inference: Maximum Likelihood - idea, properties, connection to OLS, resampling methods - the Bootstrap
  • Causality: potential outcomes, experiment/non-experiment, Directed Acyclic Graphs (DAGs), non-parametric identification via DAGs, quasi-experimental examples, Instrumental variables, source of exogenous variation, Matching, Difference-in-Differences, Regression Discontinuity
Literature
    required literature
  • Adams, Christopher P. Learning Microeconometrics with R. CRC Press, 2020.
  • Lewbel, Arthur. "The identification zoo: Meanings of identification in econometrics." Journal of Economic Literature 57.4 (2019): 835-903.
  • Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. "Identification of causal effects using instrumental variables." Journal of the American statistical Association 91.434 (1996): 444-455.
  • Pearl, Judea. "Causal diagrams for empirical research." Biometrika 82.4 (1995): 669-688.
  • Hansen, Bruce, Introduction to Econometrics, chapter 10, available at: https://www.ssc.wisc.edu/~bhansen/probability/, 2021
  • Hansen, Bruce, Econometrics, available at: https://www.ssc.wisc.edu/~bhansen/probability/, 2021
  • Hansen, Bruce, Introduction to Econometrics, available at: https://www.ssc.wisc.edu/~bhansen/probability/, 2021
  • CUNNINGHAM, Scott. Causal inference: The mixtape. Yale University Press, 2021. URL info
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Sixth edition. Boston: Cengage Learning, 2016, xxi, 789. ISBN 9781305270107. info
  • FARAWAY, Julian James. Linear models with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2014, xii, 274. ISBN 9781439887332. info
    recommended literature
  • ROSSI, Richard J. Mathematical statistics : an introduction to likelihood based inference. First published. Hoboken, New Jersey: John Wiley & Sons, Inc., 2018, xvii, 422. ISBN 9781118771044. 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
  • EFRON, Bradley and Robert TIBSHIRANI. An introduction to the bootstrap. New York: Chapman & Hall, 1993, xvi, 436. ISBN 0412042312. URL info
Teaching methods
6 4-hour lectures (i.e., 24 teaching hours, 45 minutes each)
Assessment methods
Three written group-assignments (3*20% = 60%) + Written final exam (40%)
Language of instruction
English
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2021, recent)
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