157-0460/01 – Econometrics (EKOk)
| 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 | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The aim of the course is to master the process of econometric modeling with a focus on economic interpretation, model verification and its subsequent use in micro and macro management and decision making.
Teaching methods
Lectures
Tutorials
Summary
1. Introduction to econometrics (definition of econometrics, relation to other scientific disciplines, clarification of basic concepts, origin of econometrics, process of econometric modeling).
2. Time series analysis (types of time series, methods of time series, decomposition of time series, regression analysis, model verification).
3. Simple linear regression model (meaning of regression analysis, population versus selective regression line).
4. Least Squares Method (LSM, fit of regression line to data, assumptions of classical simple regression model and their verification).
5. Multiple regression model (definition of classical multivariate linear regression model, assumptions, matrix notation, corrected determination coefficient).
6. Statistical verification (regression coefficients, model as a whole).
7. Econometrical verification - autocorrelation, heteroskedasticity, multicolinearity, normality, model specification.
8. Functional forms (exponential model, LIN-LOG model, LOG-LIN model, reciprocal model) + economic interpretation.
9. Prediction (prediction error, point or interval prediction, ex-post and ex-ante prediction).
10. Techniques of artificial variables - dummy variables.
11. Panel data (definition of panel models, fixed effects (time or space, level constants or slope coefficient), random component effect).
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Credit:
- active participation in seminars, presentation of the project topic,
- processing of the project according to the required structure and submission in LMS Moodle.
- getting at least 23 points out of 45.
Exam:
- oral - defense of the project and oral questions from the given topics in LMS Moodle.
E-learning
Students have all presentation, case studies, assignments and exercise data in LMS Moodle.
LMS Moodle
Other requirements
Submission of the processed project by the end of January.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to econometrics (definition of econometrics, relation to other scientific disciplines, clarification of basic concepts, origin
econometrics, process of econometric modeling)
2. Analysis of time series (types of time series (TS), methods of analysis of TS, decomposition of TS, Box-Jenkins methodology, regression analysis, spectral analysis, model verification)
3. Simple linear regression model (meaning of regression analysis, population versus selective regression line, essence of least squares approximation (LSA), fit of regression line to data, assumptions of classical simple regression model and their verification)
4. Multiple regression model_1 (definition of classical multivariate linear regression model (CMLRM), assumptions of CMLRM, matrix notation of CMLRM, corrected determination coefficient)
5. Multiple regression model_2 (residue normality testing)
6. Statistical verification (regression coefficients, model as a whole)
7. Econometric verification - problem of autocorrelation
8. Econometric verification - problem of heteroskedasticity
9. Econometric verification - problem of multicolinearity
10. Economic verification - model specification
11. Prediction (error of prediction, point or interval prediction, ex-post and ex-ante prediction, mean value prediction or individual values prediction of the explained variable)
12. Functional forms (exponential model, LIN-LOG model, LOG-LIN model, reciprocal model)
13. Technique of artificial variables (qualitative or discrete character of factors and technique of artificial variables, ANOVA models, ANCOVA models, regression models with 1 quantitative and 1 qualitative variable with broader scale, application of technology artificial variables)
14. Panel data (definition of panel models, fixed effects (time or space, level constants or slope constant), random effects)
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.