## DXE_EMT2 Econometrics 2

Ekonomicko-správní fakulta
jaro 2024
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 2. 4.
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
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 2023, jaro 2025.
• Statistika zápisu (nejnovější)