157-0560/01 – Econometrics (ECON)

Gurantor departmentDepartment of System EngineeringCredits5
Subject guarantorprof. Ing. Jana Hančlová, CSc.Subject version guarantorprof. Ing. Jana Hančlová, CSc.
Study levelundergraduate or graduateRequirementCompulsory
Year1Semesterwinter
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesEKFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
CHY0034 Mgr. Ing. Lucie Chytilová, Ph.D.
HAN60 prof. Ing. Jana Hančlová, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

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:

GUJARATI, Damodar N. Basic Econometrics. 4th ed. Singapore: Mc Graw-Hill, 2003, 1002 s. ISBN 0-07-233542-4. WOOLDRIDGE, Jeffrey M. Introductory Econometrics: A Modern Approach. 4th ed. Mason. Ohio: South Western Cengage Learning, 2008. 912 pp. ISBN 978-0-324-58162-1. RAMANATHAN, Ramu. Introductory Econometrics with Applications. 5th edition. Harcourt College Publishers, 2002. ISBN-13: 978-0030343421.

Recommended literature:

GREENE, William.H. Econometric Analysis. Pearson Education, 2008. ISBN 9780135137406. HEIJ, CH. et al: Econometrics Methods with Applications in Business and Economics. Oxford: Oxford University Press, 2004. ISBN 0-19-926801-0. WONNACOTT, R. J. and WONNACOTT, T.H. Econometrics. Florence Taylor and Francis Ann Arbor, Michigan ProQuest 2014.

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

Další požadavky na studenta

- participation in exercises 80%, - ongoing project work, - 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

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  25
        Examination Examination 55  28
Mandatory attendence parzicipation: - active participation in exercises 80 %, - presentation of the project topic within the 4th week of teaching, - 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.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N0311A050012) Applied economics (S01) International Economic Relations P Czech Ostrava 1 Compulsory study plan
2019/2020 (N0311A050013) Applied Economics (S01) Economic Development P English Ostrava 1 Compulsory study plan
2019/2020 (N0412A050005) Finance P English Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner