157-0588/02 – Introduction to Econometrics (INECON)
| Gurantor department | Department of Systems Engineering and Informatics | Credits | 5 |
| Subject guarantor | prof. Ing. Jana Hančlová, CSc. | Subject version guarantor | prof. Ing. Jana Hančlová, CSc. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory |
| Year | 3 | Semester | winter |
| | Study language | English |
| Year of introduction | 2015/2016 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Bachelor |
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
Project work
Summary
The aim of the course is to understand and master the process of econometric analysis of economic behavior of individual entities (eg companies) using cross-sectional resp. panel econometric modeling.
Compulsory literature:
Recommended literature:
1. KOOP, Gary, ed. Bayesian Econometric Methods (Econometric Exercises). Cambridge University Press, 2019. 376 s. ISBN-13: 978-1108437493.
2. HEISS, Florian. Using R for Introductory Econometrics. CreateSpace Independent Publishing Platform, 2020, 378 s. ISBN-13: 978-1523285136.
3. HEISS, Florian. Using Python for Introductory Econometrics. CreateSpace Independent Publishing Platform, 2020. 428 s. ISBN-13: 979-8648436763.
Additional study materials
Way of continuous check of knowledge in the course of semester
Attendance
• lectures – 60 %
• exercises – 80 %
Project
• submission - printed version to teacher + electronic version into LMS (until January 31, 2019 to obtain 23-45 points
• maximum point is 45, required number of points to pass credit is 23.
Final oral exam
• 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,
• minimum number of points to pass the final examination is 28, maximum points of project presentation and the oral exam is 55.
Minimum number of points to pass Econometrics course is 51, maximum is 100.
The term econometrics 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 structure 1 week before exam to LMS.
The structure of the project:
1. Introduction, contents, goal of your project. (max 2 points)
2. Statement of theory or hypothesis for economic model. (max 2 points)
3. Data sources, data analysis, data graph and description of the development. (max 4 points)
4. Estimation of the econometric model using example data. (max 3 points)
5. Statistical verification of the parameters and model. (max 5 points)
6. Econometric verification of the model (checking the underlying assumptions autocorrelation (max 5 points), heteroscedasticity (max 5 points), multicollinearity (max 3 points), normality (max 3 points).
7. Specification of the econometric model. (max 4 points).
8. Economic verification and interpretation. (max 4 points)
E-learning
LMSMoodle:
https://lms.vsb.cz/course/view.php?id=132780
Other requirements
The conditions for completing the course are to obtain at least 23 points from the credit project, participation in the exercises at least 80%, oral exam at least 28 points out of 55, ie to obtain 51 points out of 100.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Time series analysis (basic characteristics, graphical analysis, time series transformation, time series decomposition).
2. Linear regression model (formulation, estimation, specification, assumptions, MNC)
3. Verification of the estimated model (statistical verification, autocorrelation, heteroskedasticity, multicollinearity, economic verification).
4. Prediction (classification of forecasts, point and interval prediction, ex-post and ex-ante prediction, prediction accuracy).
5. Testing the normality of the residual component (graphical assessment, sophisticated statistical tests).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction