DXE_EMTR Econometrics

Ekonomicko-správní fakulta
podzim 2021
Rozsah
24/0/0. 12 kr. Ukončení: zk.
Vyučující
Mgr. Lukáš Lafférs, PhD. (přednášející)
doc. Ing. Štěpán Mikula, Ph.D. (pomocník)
Garance
doc. Ing. Daniel Němec, Ph.D.
Katedra ekonomie – Ekonomicko-správní fakulta
Kontaktní osoba: Mgr. Lucie Přikrylová
Dodavatelské pracoviště: Katedra ekonomie – Ekonomicko-správní fakulta
Rozvrh
Čt 30. 9. 14:00–17:50 S307, Pá 1. 10. 9:00–12:50 S307, Čt 11. 11. 14:00–17:50 S307, Pá 12. 11. 9:00–12:50 S307, Čt 6. 1. 14:00–17:50 S307, Pá 7. 1. 9:00–11:00 S307, 12:00–14:00 S307
Předpoklady
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)
Omezení zápisu do předmětu
Předmět je určen pouze studentům mateřských oborů.
Mateřské obory/plány
předmět má 26 mateřských oborů, zobrazit
Cíle předmětu
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.
Výstupy z učení
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.
Osnova
  • 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
Literatura
    povinná literatura
  • 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
    doporučená literatura
  • 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 a Jörn-Steffen PISCHKE. Mostly harmless econometrics : an empiricist's companion. Princeton: Princeton University Press, 2009, xiii, 373. ISBN 9780691120355. URL info
  • EFRON, Bradley a Robert TIBSHIRANI. An introduction to the bootstrap. New York: Chapman & Hall, 1993, xvi, 436. ISBN 0412042312. URL info
Výukové metody
6 4-hour lectures (i.e., 24 teaching hours, 45 minutes each)
Metody hodnocení
Three written group-assignments (3*20% = 60%) + Written final exam (40%)
Vyučovací jazyk
Angličtina
Další komentáře
Studijní materiály
Předmět je dovoleno ukončit i mimo zkouškové období.
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2009, podzim 2010, podzim 2011, podzim 2012, podzim 2013, podzim 2014, podzim 2015, podzim 2016, podzim 2017, podzim 2018, podzim 2019, podzim 2020, podzim 2022, podzim 2023, podzim 2024.