DXE_EMT2 Econometrics 2

Faculty of Economics and Administration
Spring 2024
Extent and Intensity
24/0/0. 12 credit(s). Type of Completion: z (credit).
doc. Ing. Daniel Němec, Ph.D. (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
Tue 18:00–19:30 MT205, except Tue 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).
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 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.
Learning outcomes
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.
  • 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.
    required literature
  • GREENE, William H. Econometric analysis. Eighth edition. Harlow, England: Pearson, 2020, 1166 stran. ISBN 9781292231136. info
  • DEJONG, David N. and 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 and 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
    recommended literature
  • GREENE, William H. Econometric analysis. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2008, xxxvii, 11. ISBN 9780135132456. info
Teaching methods
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.
Assessment methods
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.
Language of instruction
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
Information on course enrolment limitations: Předmět se bude vyučovat, pokud si jej zapíše min. 5 studentů.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.
  • Enrolment Statistics (recent)
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