DXE_EMT2 Econometrics 2

Ekonomicko-správní fakulta
jaro 2023
Rozsah
24/0/0. 12 kr. Ukončení: z.
Vyučující
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
Út 18:00–19:30 MT205, kromě Út 28. 3.
Předpoklady
Participants should be familiar with the following topics:
*Linear algebra – linear equations, matrices, vectors (basic operations and properties).
*Descriptive statistics – measures of central tendency, measures of dispersion, measures of association, histogram, frequency tables, scatterplot, quantiles
*Theory of probability – probability and its properties, random variables and distribution functions in one and several dimensions, moments, convergence of random variables, limit theorems, law of large numbers.
*Mathematical statistics – point estimation, confidence intervals for parameters of normal distribution, hypothesis testing, p-value, significance level.
*Basic econometrics - ordinary least squares method, linear regression, classical assumptions and their violations
These topics correspond to the chapters the appendices of Verbeek’s book, in particular, to the chapters 1-5 and sections: A1, A2, A3, A4, A6, A8, B1, B2, B3 (excluding Jensen's inequality), B4, B5, B6 and B7 (excluding some properties of the chi-squared distribution and the F-distribution).
Omezení zápisu do předmětu
Předmět je určen pouze studentům mateřských oborů.

Jiné omezení: Předmět se bude vyučovat, pokud si jej zapíše min. 5 studentů.
Mateřské obory/plány
předmět má 26 mateřských oborů, zobrazit
Cíle předmětu
The course is intended to provide the students with the advanced topics of econometrics and economic modelling: maximum likelihood estimation, econometric methods for empirical analysis of time series data, methods and techniques of DSGE modelling, models for analyzing limited dependent variables, panel data econometrics.
Výstupy z učení
The course is designed to provide students with a good knowledge of basic and advanced econometric tools so that:
- they will be able to apply these tools to modeling, estimation, inference, and forecasting in the context of economic problems;
- they have experience in applying the econometric software for analyzing data;
- they can evaluate critically the results from others who use econometric methods and tools;
- they have a basis for further studies of econometric literature.
Osnova
  • Lectures (and the corresponding assigned reading) will be chosen with respect to the research topics of the students enrolled on the course. The lectures may cover the following topics:
  • 1. Methods and techniques of Bayesian analysis.
  • 2. Methodology of DSGE modelling.
  • 3. State-space models and Kalman filter.
  • 4. Advanced approaches in panel data modelling.
  • 5. Introduction to spatial econometrics.
  • 6. Modern tools and techniques of macroeconometrics.
  • 7. Modern tools and techniques of microeconometrics.
Literatura
    povinná literatura
  • GREENE, William H. Econometric analysis. Eighth edition. Harlow, England: Pearson, 2020, 1166 stran. ISBN 9781292231136. info
  • DEJONG, David N. a Chetan DAVE. Structural macroeconometrics. Second edition. Princeton: Princeton University Press, 2011, xvi, 418. ISBN 9780691152875. info
  • COSTA, Celso. Understanding DSGE. Wilmington: Vernon Press, 2016, x, 269. ISBN 9781622731336. info
  • BALTAGI, Badi H. Econometric analysis of panel data. Fifth edition. Chichester: Wiley, 2013, xiii, 373. ISBN 9781118672327. info
  • CAMERON, Adrian Colin a P. K. TRIVEDI. Microeconometrics : methods and applications. 1st ed. Cambridge: Cambridge University Press, 2005, xxii, 1034. ISBN 0521848059. info
  • GREENE, William H. Econometric analysis. 7th ed. Boston: Pearson, 2012, 1228 s. ISBN 9780273753568. info
    doporučená literatura
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Výukové metody
12 lectures á 2 hours (i.e., 24 teaching hours, 45 minutes each), class discussion, homework including computer exercises using Gretl, and presentation of homework by participants; course language is English.
Metody hodnocení
For grading, written homework, presentation of homework in class, and a final oral exam will be taken into account. The weight for homework will be 50 %, that of the oral final exam 50 %. Presentation of homework in class means that students must be prepared to be called at random to the blackboard.
Vyučovací jazyk
Angličtina
Další komentáře
Předmět je dovoleno ukončit i mimo zkouškové období.
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích jaro 2010, jaro 2011, jaro 2012, jaro 2013, jaro 2014, jaro 2015, jaro 2016, jaro 2018, jaro 2019, jaro 2020, jaro 2021, jaro 2022, jaro 2024, jaro 2025, jaro 2026.