157-0360/03 – Econometrics (EKON)
| Gurantor department | Department of Systems Engineering and Informatics | Credits | 4 |
| Subject guarantor | prof. Ing. Jana Hančlová, CSc. | Subject version guarantor | prof. Ing. Jana Hančlová, CSc. |
| Study level | undergraduate or graduate | | |
| | Study language | Czech |
| Year of introduction | 2014/2015 | Year of cancellation | 2020/2021 |
| 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 acquire the process of econometric modeling with a focus on model formulation, analysis and transformation of input time series, verification of the estimated model, economic interpretation of the results, and their practical application in management and decision-making at the micro or macro level.
Teaching methods
Lectures
Tutorials
Project work
Summary
1. Introduction to Econometrics
Definition of econometrics, its relationship to other scientific disciplines, explanation of basic concepts, origins of econometrics, and the process of econometric modeling.
2. Time Series Analysis
Types of time series, methods of time series analysis, decomposition of time series, regression analysis, and model verification.
3. Simple Linear Regression Model
Importance of regression analysis, population vs. sample regression line, the principle of the least squares method, goodness of fit, assumptions of the classical simple regression model and their testing.
4. Multiple Regression Model
Definition of the classical multivariate linear regression model, assumptions, matrix notation, and adjusted coefficient of determination.
5. Statistical Verification
Testing of regression coefficients and the model as a whole.
6. Econometric Verification
Detection and treatment of autocorrelation, heteroskedasticity, multicollinearity, normality, and model specification errors.
7. Functional Forms
Exponential model, LIN-LOG model, LOG-LIN model, reciprocal model, and economic interpretation of regression parameters.
8. Forecasting
Forecast error, point and interval forecasts, ex-post and ex-ante forecasting.
9. Dummy Variable Technique
10. Panel Data
Definition of panel data models and fixed effects.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
- Elaboration and defense of project with respect to the required structure.
- Inserting of this project into the LMS and obtaining a simple majority of the number of points.
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