157-0587/03 – Econometrics (ECON)
Gurantor department | Department of Systems Engineering | Credits | 5 |
Subject guarantor | prof. Ing. Jana Hančlová, CSc. | Subject version guarantor | prof. Ing. Jana Hančlová, CSc. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 1 | Semester | winter |
| | Study language | English |
Year of introduction | 2012/2013 | Year of cancellation | 2022/2023 |
Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The basic aim of the course is to provide some of the developments in the theory and practice of econometrics in economics using statistical package SPSS. Students will discuss the steps involved in traditional econometric methodology – statement of theory or hypothesis, specification of mathematical and econometric models, obtaining and analysing data, estimation of the econometric models, statistical hypothesis testing, econometric verification of the models ( multicollinearity, heteroscedasticity, autocorrelation problem, normality of the disturbances). Attention is devoted the questions of the functional form of the regression models. Students will be introduced to the possibilities of econometric models applications for prediction, control or policy purposes.
Teaching methods
Lectures
Individual consultations
Tutorials
Project work
Summary
1. Introduction to econometrics (subject of econometrics, methodology of econometrics)
2. Simple linear regression function (the nature of regression analysis, the concept of population and sample regression function (deterministic and stochastic version), the method of ordinary least squares, coefficient of determination)
3. Statistical verification (testing of regression coefficients, the overall of sample regression model)
4. Autocorrelation (the nature, the consequences of autocorrelation, detection, removing).
5. Heteroscedasticity (the nature, its consequences, detection, removing, WOLS)
7. Multicollinearity (the nature, its consequences, detection, removing).
8. Model specification (model selection criteria, types of specification errors, consequences, tests)
9. Prediction (ex-post and ex-ante, mean and individual prediction, point and interval prediction).
10. Functional form of regression models (exponential regression model, semi log models and reciprocal models).
11. Dummy variable regression models.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Preparation of the semester project and its defense.
E-learning
LMS Moodle
Other requirements
Course requirement summary
Attendance
• exercises – 80%
Project
• the individual term project - passing term project is compulsory in order to get to the examination stage and obtaining the final grade:
• submitting the individual term project in the required with given structure 1 week before exam to LMS, on January 31, 2018 at the least.
• maximum number of points is 45, required number of points to pass credit is 23.
Project structure:
1. Introduction, contents, goal of your project. (max 1 points)
2. Statement of theory or hypothesis for economic model. (max 3 points)
3. Specification of the econometric model. (max 3 points)
4. Data sources, data analysis, data graph and description of the development. (max 4 points)
5. Estimation of the econometric model using example data. (max 3 points)
6. Statistical verification of the parameters and model. (max 4 points)
7. Econometric verification of the model (checking the underlying assumptions autocorrelation (max 6 points), heteroscedasticity (max 6 points), multicollinearity (max 4 points)), normality of residuals (max 3 points)
8. Economic verification and interpretation. (max 4 points)
9. Using your model for prediction. (max 3 points)
10. Appreciation of your results in your project. (max 1 point)
11. Literature
I advise you to start as soon as possible and work on your project as we proceed with classes.
FINAL EXAMINATION STAGE:
• if the student submitted the term project and obtains more than 23 points, he/she will be allowed to go for the final examination,
• student will be asked to present his/her project, to justify the choice of the project and explain the procedure and his/her results
• additional questions from the course outline will be asked,
• maximum number of points of the oral exam is 55,
• minimum number of points to pass the final examination is 28.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to econometrics (subject of econometrics, methodology of econometrics)
2. Simple linear regression function (the nature of regression analysis, the concept of population and sample regression function (deterministic and stochastic version), the method of ordinary least squares, coefficient of determination)
3. Statistical verification (testing of regression coefficients, the overall of sample regression model)
4. Autocorrelation (the nature, the consequences of autocorrelation, detection, removing).
5. Heteroscedasticity (the nature, its consequences, detection, removing, WOLS)
7. Multicollinearity (the nature, its consequences, detection, removing).
8. Model specification (model selection criteria, types of specification errors, consequences, tests).
9. Normality of residuals.
10. Prediction (ex-post and ex-ante, mean and individual prediction, point and interval prediction).
11. Functional form of regression models (exponential regression model, semi-log models and reciprocal models).
12. Dummy variable regression models.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction