CXE_ZESR_M Fundamentals of econometrics in the R system

Faculty of Economics and Administration
Spring 2024
Extent and Intensity
0/0/0. 4 credit(s). Type of Completion: k (colloquium).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
Ing. Mgr. Vlastimil Reichel, Ph.D. (assistant)
Mgr. Jakub Chalmovianský, Ph.D. (assistant)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: doc. Ing. Daniel Němec, Ph.D.
Supplier department: Department of Economics – Faculty of Economics and Administration
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
This course is an introductory course in applied econometrics focusing on a non-technical introduction to regression (linear regression model and least squares method), principles of econometric modelling (model specification, formulation and hypothesis testing), basic diagnostic tests of regression analysis, and models of qualitative dependent variables (linear probability model, logit and probit models, their applications and diagnostics). The course is mainly application-oriented and uses the freely available R software (no prior knowledge of R is required to take the course. Although this is an introductory course in applied econometrics, methods, tools and procedures reflecting current trends in working with real data and problems based on them are included at the application level.
The course is designed for those who want to learn how to use basic or slightly more advanced econometric techniques and use them to perform an appropriate empirical real data analysis, interpret the results meaningfully, formulate relevant and testable econometric models and understand the basic principles of econometric modelling. In addition to the presentation of standard techniques, the course also introduces current trends in econometric modelling applied in practice. The course does not require prior knowledge of econometrics, statistics or programming. Graduates of the course will gain a good foundation for performing flawless data analysis and finding correlations between observed variables. Mastering these universally applicable methods represents a tremendous competitive advantage in the labour market. Obtained skills and knowledge can help better understand the principles of even more advanced data analysis techniques and procedures. Graduates of the course will also learn more general skills for working with robust and freely available R software.
Learning outcomes
The course is designed to help participants understand why econometrics is useful and give them some practical experience using standard econometric tools. After completion of the course, participants:
will be able to apply these tools in modelling, estimation, analysis, and prediction in the context of real-world economic problems,
be able to critically evaluate the results and conclusions of others using econometric tools,
acquire the necessary foundations for further study of econometrics.
gain an overview of existing advanced techniques covered by the follow-up and extension courses.
Syllabus
  • 1. Linear regression model (non-technical introduction to regression, multiple regression model, dummy variables and their applications)
  • 2. Classical assumptions, model formulation and hypothesis testing (classical assumptions and their intuitive meaning, econometric model building, hypothesis testing)
  • 3. Violations of classical assumptions and diagnostic tests (heteroskedasticity, autocorrelation, non-normality, introduction to the endogeneity problem)
  • 4. Discrete choice models (linear probability model, logit model, probit model, quality assessment of discrete choice models)
Literature
    required literature
  • HEISS, Florian. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. info
    recommended literature
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. info
  • BÉKÉS, Gábor and Gábor KÉZDI. Data analysis for business, economics, and policy. First published. Cambridge: Cambridge University Press, 2021, xxiii, 714. ISBN 9781108483018. info
  • KOOP, Gary. Introduction to econometrics. Chichester: John Wiley & Sons, 2008, 371 s. ISBN 9780470032701. info
  • HILL, R. Carter, William E. GRIFFITHS and Guay C. LIM. Principles of econometrics. Fifth edition. Hoboken: Wiley Custom, 2018, xxvi, 878. ISBN 9781119510567. info
Teaching methods
The course is divided into four thematic study blocks (with a time interval of one week, which is intended for self-study), which are then further divided into particular topics (all this is incorporated into the interactive syllabus within the four main chapters and the individual subchapters incorporated into them). A brief introduction to working with R introduces these four basic blocks.
Each subtopic contains illustrative, annotated examples, essential motivation, and intuitive explanations of the issues. The conclusion of each block includes assignments of examples to work on separately.
After each topic block is completed, participants have the opportunity to participate in an online consultation on all the issues and problem sets discussed in the block, which includes an outline of the following topic block (for the first topic block, the online consultation at the beginning of the course serves this purpose).
Assessment methods
Individual completion of assigned exercises (with comments) for each thematic block. To obtain micro-credentials, it is necessary to prepare two further assigned tasks based on the discussed thematic blocks and to prepare and defend the final project in an online presentation at the end of the course. The obtained microcredentials can be accepted for recognising the BKE_ZAEE Introduction to Econometrics (for occupational studies).
Language of instruction
Czech
Further Comments
The course is taught only once.
The course is taught: in blocks.
  • Enrolment Statistics (recent)
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