ESF:DXE_EMTR Ekonometrie - Informace o předmětu

DXE_EMTR Econometrics

Rozsah

0/24/0. 12 kr. Ukončení: zk.
Vyučováno kontaktně

Vyučující

Mgr. Lukáš Lafférs, PhD. (přednášející)
doc. Ing. Daniel Němec, Ph.D. (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 26. 9. 13:00–15:00 MT205, 16:00–18:00 MT205, Pá 27. 9. 9:00–11:00 MT205, 12:00–14:00 MT205, Čt 31. 10. 13:00–15:00 MT205, 16:00–18:00 MT205, Pá 1. 11. 9:00–11:00 MT205, 12:00–14:00 MT205, Čt 28. 11. 13:00–15:00 MT205, 16:00–18:00 MT205, Pá 29. 11. 9:00–11:00 MT205, 12:00–14:00 MT205

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

• Business Economy and Management (program ESF, D-PEMA) (2)
• Economic Policy (program ESF, D-HOSPA) (2)
• Economics (program ESF, D-EKONA) (2)
• Ekonomie (program ESF, D-EKON) (2)
• Finance (program ESF, D-FIN) (2)
• Finance (program ESF, D-FINA) (2)
• Finanzwesen (program ESF, D-FINN) (2)
• Hospodářská politika (program ESF, D-HOSP) (2)
• Podniková ekonomika a management (program ESF, D-PEM) (2)
• Public Economics (program ESF, D-VEEKA) (2)
• Regional Economics (program ESF, D-REEKA) (2)
• Regionální ekonomie (program ESF, D-REEK) (2)
• Veřejná ekonomie (program ESF, D-VEEK) (2)

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
• Logistic regression:
• 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
• 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.
• Adams, Christopher P. Learning Microeconometrics with R. CRC Press, 2020.
• Hansen, Bruce, Introduction to Econometrics, chapter 10, 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
• Hansen, Bruce, Econometrics, available at: https://www.ssc.wisc.edu/~bhansen/probability/, 2021
• Lewbel, Arthur. "The identification zoo: Meanings of identification in econometrics." Journal of Economic Literature 57.4 (2019): 835-903.
• CUNNINGHAM, Scott. Causal inference: The mixtape. Yale University Press, 2021. URL info
• FARAWAY, Julian James. Linear models with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2014, xii, 274. ISBN 9781439887332. info
• WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. 4th ed. (International stude. Canada: South-Western, 2009, xx, 865. ISBN 9780324585483. 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

12 2-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

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