151-0517/01 – Econometrics (ECON)
| Gurantor department | Department of Mathematical Methods in Economics | 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 | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2010/2011 | Year of cancellation | 2016/2017 |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The goal is to:
- be able to describe and apply the process of analyzing of economic time series,
- understand the process of modeling the behavior of economic system based on regression analysis,
- select and use appropriate econometrics methodology - the formulation, estimation, prediction and verification of modeled systems,
- explain the context of the theoretical behavior of economic systems modeled with empirical results and make appropriate modification of your model,
- use the estimated regression models for forecasting.
Teaching methods
Lectures
Individual consultations
Tutorials
Summary
1. Time series analysis (the basic characteristics, graphical time series analysis, time series transformation, decomposition of time series)
2. Linear regression models (model formulation, estimation, specification, assumptions, OLS methods)
3. Verification of the estimated regression model (statistical verification, autocorrelation, heteroscedasticity, multicollinearity, economic verification).
4.Forecasting (prediction typology, point and interval prediction, prediction of ex-post and ex ante, forecasting accuracy rate).
5.Testing of residual normality (graphical tests, sophisticated tests).
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
A. Project Assignments (min 26 points/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. Econometric verification of the model (multicollinearity, autocorrelation, heteroscedasticity)
8. Economic verification and interpretation.
9. Using your model for prediction.
10. Appreciation of your results in your project.
11. Literature
B. Project presentation
C. Exam
E-learning
Other requirements
A. Project Assignments (min 26 points/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. Econometric verification of the model (multicollinearity, autocorrelation, heteroscedasticity)
8. Economic verification and interpretation.
9. Using your model for prediction.
10. Appreciation of your results in your project.
11. Literature
B. Project presentation
C. Exam
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to econometrics ( subject of economterics, metodology 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 od 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 (exponencial regression model, semilog models, reciprocal models).
11. Dummy variable regression models.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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