DXE_EMTR Econometrics

Faculty of Economics and Administration
Autumn 2020
Extent and Intensity
24/0/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
Prof. Dr. Peter Hackl (lecturer)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (assistant)
Guaranteed by
prof. Ing. Osvald Vašíček, CSc.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Lucie Přikrylová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Fri 9. 10. 13:00–14:00 S307, Fri 16. 10. 10:00–14:00 S307, Fri 23. 10. 10:00–14:00 S307, Fri 30. 10. 10:00–14:00 S307, Fri 6. 11. 10:00–14:00 S307, Fri 13. 11. 10:00–14:00 S307, Fri 20. 11. 10:00–14:00 S307, Fri 27. 11. 10:00–14:00 S307, Fri 4. 12. 10:00–14:00 S307, Fri 11. 12. 10:00–14:00 S307, Fri 18. 12. 10:00–14:00 S307
Prerequisites
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.
These topics correspond to the appendices of Verbeek’s book, in particular, to the 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 introduces students to common used econometric tools and techniques. Students shall gain sufficient knowledge and experience for his/her independent and qualified work with empirical data. The student should be able to formulate correctly, to identify economic models and to interpret the results accordingly.
Learning outcomes
Students shall gain sufficient knowledge and experience to:
- independently work with empirical data;
- formulate economic and econometric model correctly;
- identify econometric models;
- interpret the results accordingly;
- understand actual academic papers using econometric techniques;
- apply obtained knowledge of econometric theory for further study of advanced fields of econometrics.
Syllabus
  • 1. Introduction to linear regression model (Verbeek, Ch. 2) – normal linear regression model, least squares method, properties of OLS estimators;
  • 2. Introduction to linear regression model (Verbeek, Ch. 2) – goodness of fit, hypotheses testing, multicollinearity;
  • 3. Interpreting and comparing regression models (Verbeek, Ch.3) – interpretation of the fitted model, selection of regressors, testing the functional form;
  • 4. Heteroskedascity and autocorrelation (Verbeek, Ch. 4) – causes, consequences, testing, alternatives for inference;
  • 5. Endogeneity, instrumental variables and GMM (Verbeek, Ch. 5) – the instrumental variables estimator, the generalized instrumental variables estimator, the Generalized Method of Moments (principles and examples of use);
  • 6. The practice of econometric modeling
Literature
    required literature
  • VERBEEK, Marno. A guide to modern econometrics. Fifth edition. Hoboken: Wiley Custom, 2017, xii, 508. ISBN 9781119472117. info
    recommended literature
  • KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008, xii, 585. ISBN 9781405182584. info
Teaching methods
6 lectures á 3 hours (i.e., 24 teaching hours, 45 minutes each), class discussion, homeworks (computer exercises using Gretl) and presentation of homeworks by participants
Assessment methods
For grading, written homework, presentation of homework in class and a final written exam will be of relevance. The weights are as follows: homework with 40%, final exam (consisting of theoretical and practical part) with 60%. The presentation of homework in class means that students must be prepared to be called at random. Minimal requirements to pass final exam are as follows: 60%.
Language of instruction
English
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2020, recent)
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