157-0587/01 – 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 | | |
| | Study language | English |
| Year of introduction | 2012/2013 | Year of cancellation | 2013/2014 |
| 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
A. Project Assignments + quizzes (min 26 points/max 53) :
1. Introduction, contents, goal of your project.
2. Statement of theory or hypothesis for economic model.
3. Specification of the mathematical model, formulation of the econometric model.
4. Data sources, data analysis, data graph and description of the development.
5. Estimation of the econometric model using example data.
6. Statistical verification of the parameters and model.
7. Misspecification.
8. Econometric verification of the model (multicollinearity, autocorrelation, heteroscedasticity)
9. Economic verification and interpretation.
10. Appreciation of your results in your project.
11. Literature
B. Project presentation + C. Exam (B+C oral exam min 24/ max 47).
E-learning
Other requirements
A. Project Assignments + quizzes (min 26 points/max 53) :
1. Introduction, contents, goal of your project.
2. Statement of theory or hypothesis for economic model.
3. Specification of the mathematical model, formulation of the econometric model.
4. Data sources, data analysis, data graph and description of the development.
5. Estimation of the econometric model using example data.
6. Statistical verification of the parameters and model.
7. Misspecification.
8. Econometric verification of the model (multicollinearity, autocorrelation, heteroscedasticity)
9. Economic verification and interpretation.
10. Appreciation of your results in your project.
11. Literature
B. Project presentation + C. Exam (B+C oral exam min 24/ max 47).
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. 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.
Conditions for subject completion
Conditions for completion are defined only for particular subject version and form of study
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.