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Econometric generalizations of linear regression (heteroscedasticity, serial autocorrelation). Special regression
problems in econometrics (multicollinearity, nonlinear regression, model stability). Estimation methods in the
regression model. Dynamic econometric models. Econometric systems of equations.
Last update: Omelka Marek, doc. Ing., Ph.D. (02.12.2020)
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Students will gain an understanding of the mathematical foundations of fundamental and selected advanced econometric methods and develop the ability to apply these methods independently. Last update: Hudecová Šárka, doc. RNDr., Ph.D. (25.08.2026)
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To complete the course, it is necessary to obtain credit from the exercises and to pass the exam.
Conditions for obtaining credit:
Four homework assignments and four reviews will be assigned. A total of 20 points can be obtained (4 points for each homework assignment and 1 point for each review).
To obtain the course credit, students must earn at least 7 points from the first two homework assignments and the first two reviews, and at least 7 points from the last two homework assignments and the last two reviews.
If a student earns fewer than 7 points in either of these two parts, they are entitled to revise one of the homework assignments from the respective part once, provided that the assignment was duly submitted by the prescribed deadline. In such a case, the higher of the scores awarded for the original and revised versions will count. Thus, at most two homework assignments may be revised in total, one from each part.
Exam:
Obtaining the course credit is a prerequisite for taking the examination.
The examination requirements correspond to the course syllabus to the extent covered in the lectures. Students are expected to know all essential definitions, statements, and basic proofs. They are also required to be able to apply the theory to specific examples.
The examination consists of a written and an oral part. Students must successfully pass the written part in order to proceed to the oral part. Both parts must be passed in order to successfully complete the examination. The final grade is based on the results of both parts. If a student fails the oral part, both the written and oral parts must be retaken at the next examination attempt.
The oral part of the examination may also include a discussion of the methods used in solving the homework assignments.
Last update: Hudecová Šárka, doc. RNDr., Ph.D. (25.08.2026)
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Green, W.H (2011): Econometric Analysis. Prentice Hall, New Yersey (7. vydání) Wooldridge, J.M. (2020): Introductory Econometrics" A Modern Approach. Cengage, Boston (7. vydání).
Last update: Hudecová Šárka, doc. RNDr., Ph.D. (25.08.2026)
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Lecture + exercises. Last update: Omelka Marek, doc. Ing., Ph.D. (30.11.2020)
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Obtaining the course credit is a prerequisite for taking the examination.
The examination requirements correspond to the course syllabus to the extent covered in the lectures. Students are expected to know all essential definitions, statements, and basic proofs. They are also required to be able to apply the theory to specific examples.
The examination consists of a written and an oral part. Students must successfully pass the written part in order to proceed to the oral part. Both parts must be passed in order to successfully complete the examination. The final grade is based on the results of both parts. If a student fails the oral part, both the written and oral parts must be retaken at the next examination attempt.
The oral part of the examination may also include a discussion of the methods used in solving the homework assignments. Last update: Hudecová Šárka, doc. RNDr., Ph.D. (25.08.2026)
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I. Review of linear regression. II. Regression for heteroskedastic data. III. Regression for time series data. IV. Special regression problems in econometrics (multicollinearity, nonlinear regression, IV estimator). V. Fundamentals of the econometric approach to panel data analysis. VI. Multivariate time series. Last update: Hudecová Šárka, doc. RNDr., Ph.D. (25.08.2026)
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Basic knowledge of mathematical statistics (particularly linear regression), theory of probability and random processes. Ability to solve practical projects in R program. Last update: Hudecová Šárka, doc. RNDr., Ph.D. (25.08.2026)
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