SylabUZ
| Course name | Econometrics |
| Course ID | 11.9-WK-CSEEP-E-S22 |
| Faculty | Faculty of Exact and Natural Sciences |
| Field of study | computer science and econometrics |
| Education profile | academic |
| Level of studies | First-cycle studies leading to Bachelor's degree |
| Beginning semester | winter term 2022/2023 |
| Semester | 5 |
| ECTS credits to win | 4 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 15 | 1 | - | - | Credit with grade |
| Class | 15 | 1 | - | - | Credit with grade |
The purpose of this course is to acquaint students with basics of construction and verification of statistical econometric models.
linear algebra, matematical analysis, probability theory and mathematical statistics.
Lecture:/Class/Laboratory
1. The concept of an econometric model. Classification of econometric models.
2. Graphical presentation of the idea of the least squares method on the example of a single-equation model
linear with one explanatory variable.
3. Classic linear model with many explanatory variables and its matrix form. Estimation
the least squares (LSM) method of the vector of structural parameters of this model.
4. Assumptions of the linear econometric model. Expected value and vector covariance matrix
random. Total distribution of the explained variable.
5. Properties of the OLS estimator. Gauss-Markov theorems. Unbiased component variance estimator
random.
6. Assessment of the fit of the linear model to the data.
7. Interval estimation of parameters of a linear model with many explanatory variables.
8. Verification of hypotheses regarding the linear combination of linear model parameters.
9. Methods of selecting variables for the model based on the Student's t-test.
10. Statistical verification of model assumptions.
11. Adequacy of the model.
12. Point and interval forecast.
One part of lecture is presented in the form of slides, and other in the traditional form (transformations of formulas, proofs of theorems and solutions of examples). At the classes one solves selected problems from lists. At the laboratory one solves selected problems with generated and real data with application of statitical packages .
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture grade based on exam. The grade for the exercises is based on the results from the test and
activities during classes. The laboratory grade is based on tests allowing
determine the degree of mastery of statistical tools and the ability to draw correct conclusions based on them
about the obtained analysis results.
The final grade for the course consists of the grade for the exercises (35%), the grade for the laboratory (35%) and the grade for
exam (30%). The condition for taking the exam is a positive grade from the exercises and a positive grade from
lab. The condition for passing the course is obtaining positive grades in: exercises, laboratory and
lecture.
1. A. S. Goldberger, Econometric Theory, Wiley, New York 1964.
2. J. Faraway, Linear Models with R, Chapman & Hall/CRC Texts in Statistical Science, Boca Raton Florida 2005.
3. G. S. Maddala, Introduction to Econometrics. 2nd Edition, Macmillan Publishing Company, New York 1992.
4.. W. H. Greene Econometric Analysis, Prentice Hall, Inc., New Jersey 2000.
5. G. A. F. Seber. Linear regression Analysis. John Wiley&Sons, New York 1977.
Modified by dr hab. Stefan Zontek, prof. UZ (last modification: 31-01-2024 12:57)
