| Course Unit Code | 157-0560/01 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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| Name of Lecturer(s) | Personal ID | Name |
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| HAN60 | prof. Ing. Jana Hančlová, CSc. |
| Summary |
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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). |
| Learning Outcomes of the Course Unit |
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| 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. |
| Course Contents |
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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) |
| Recommended or Required Reading |
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| Required Reading: |
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BALTAGI, Badi H. Econometrics (Classroom Companion: Economics). 6th ed. Syracuse, Ny, USA: Sysracuse University, 2021. ISBN 978-3-030-80148-9.
GUJARATI, Damodar N. Essential for Econometrics. 5th ed. Los Angeles: SAGE, 2022. ISBN 9781071850404.
WOOLDRIDGE, Jeffrey M. Introductory Econometrics: a modern approach. 7th ed. Boston: Cengage, 2020. ISBN 9781337558860.
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HANČLOVÁ, Jana. Ekonometrické modelování. Klasické přístupy s aplikacemi. Praha: Professional Publishing, 2012. 214 s. ISBN 978-80-7431-088-1.
CIPRA, Tomáš. Finanční ekonometrie. Praha: Ekopress, 2008. 538 s. ISBN 978-80-86929-43-9.
HUŠEK, Roman. Ekonometrická analýza. Praha: Oeconomica, 2007. 368 s. ISBN 978-80-245-1300-3. |
| Recommended Reading: |
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DHRYMES, Phoebus. Introductory Econometrics with contribution by John Guerard. Cham, Switzerland: Springer, 2017. ISBN 978-3319659145.
GREENE, William. H. Econometric Analysis. 8th ed. New York: Pearson Education, 2018. ISBN 978-9353061074.
HANSEN, Bruce E. Econometrics. Princeton, New Jersey: Princeton University Press, 2022. ISBN 978-0691235899.
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LUKÁČIKOVÁ, Adriana a Martin LUKÁČIK. Ekonometrické modelovanie s aplikáciami. Bratislava: Ekonóm, 2008. 343 s. ISBN 978-80-225-2614-2
REINAROVÁ, Šárka, Adéla RÁČKOVÁ a Jan ZOUHAR. Základy ekonometrie v příkladech. Praha: Oeconomica, 2009. 276 s. ISBN 978-80-245-1564-9.
GUJARATI, Damodar N. Basic Econometrics. 4th ed. Singapore: Mc Graw-Hill, 2003, 1002 s. ISBN 0-07-233542-4. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 45 | 23 |
| Examination | Examination | 55 | 28 |