| Name of course | DXE_EMTR Econometrics |
| Lecturer | Prof. Dr. Peter Hackl |
| Assistant | Ing. Daniel Němec, Ph.D. |
| ECTS | 12 credits |
| Extent and Intensity | 24 teaching hours, 45 minutes each (6 lectures á 4 hours) |
| Schedule | 5.10., 26.10., 2.11., 9.11., 23.11., 30.11; 10:00–16:00 |
| 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. |
| 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) |
| 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 | VERBEEK, Marno. A guide to modern econometrics. 4th ed. Chichester: John Wiley & Sons, 2012. xv, 497. ISBN 9781119951674. KENNEDY, Peter. A guide to econometrics. 6th ed. Malden: Blackwell, 2008. xii, 585. ISBN 9781405182584. |
| Teaching methods | 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 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%. |