157-0587/03 – Econometrics (ECON)

Gurantor departmentDepartment of System EngineeringCredits5
Subject guarantorprof. Ing. Jana Hančlová, CSc.Subject version guarantorprof. Ing. Jana Hančlová, CSc.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semesterwinter
Study languageEnglish
Year of introduction2012/2013Year 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 basic aim of the course is to provide some of the developments in the theory and practice of econometrics in economics using statistical package SPSS. Students will discuss the steps involved in traditional econometric methodology – statement of theory or hypothesis, specification of mathematical and econometric models, obtaining and analysing data, estimation of the econometric models, statistical hypothesis testing, econometric verification of the models ( multicollinearity, heteroscedasticity, autocorrelation problem, normality of the disturbances). Attention is devoted the questions of the functional form of the regression models. Students will be introduced to the possibilities of econometric models applications for prediction, control or policy purposes.

Teaching methods

Lectures
Individual consultations
Tutorials
Project work

Summary

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.

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.

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. RAMANATHAN, Ramu. Introductory Econometrics with Applications. 5th edition. Harcourt College Publishers, 2002. ISBN-13: 978-0030343421.

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

Course requirement summary Attendance • exercises – 80% Project • the individual term 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 with given structure 1 week before exam to LMS, on January 31, 2018 at the least. • maximum number of points is 45, required number of points to pass credit is 23. Project structure: 1. Introduction, contents, goal of your project. (max 1 points) 2. Statement of theory or hypothesis for economic model. (max 3 points) 3. Specification of the econometric model. (max 3 points) 4. Data sources, data analysis, data graph and description of the development. (max 4 points) 5. Estimation of the econometric model using example data. (max 3 points) 6. Statistical verification of the parameters and model. (max 4 points) 7. Econometric verification of the model (checking the underlying assumptions autocorrelation (max 6 points), heteroscedasticity (max 6 points), multicollinearity (max 4 points)), normality of residuals (max 3 points) 8. Economic verification and interpretation. (max 4 points) 9. Using your model for prediction. (max 3 points) 10. Appreciation of your results in your project. (max 1 point) 11. Literature I advise you to start as soon as possible and work on your project as we proceed with classes. FINAL EXAMINATION STAGE: • if the student submitted the term project and obtains more than 23 points, he/she will be allowed to go for the final examination, • 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, • maximum number of points of the oral exam is 55, • minimum number of points to pass the final examination is 28.

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. Normality of residuals. 10. Prediction (ex-post and ex-ante, mean and individual prediction, point and interval prediction). 11. Functional form of regression models (exponential regression model, semi-log models and reciprocal models). 12. Dummy variable regression models.

Conditions for subject completion

Full-time form (validity from: 2018/2019 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  23
        Examination Examination 55  28
Mandatory attendence parzicipation: project oral exam

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N6202) Economic Policy and Administration (6202T010) Finance P English Ostrava 2 Compulsory study plan
2018/2019 (N6202) Economic Policy and Administration (6202T027) National Economy P English Ostrava 1 Compulsory study plan
2018/2019 (N6202) Economic Policy and Administration (6202T010) Finance P English Ostrava 2 Compulsory study plan
2017/2018 (N6202) Economic Policy and Administration (6202T027) National Economy P English Ostrava 1 Compulsory study plan
2017/2018 (N6202) Economic Policy and Administration (6202T010) Finance P English Ostrava 2 Compulsory study plan
2016/2017 (N6202) Economic Policy and Administration (6202T010) Finance P English Ostrava 2 Compulsory study plan
2015/2016 (N6202) Economic Policy and Administration (6202T010) Finance P English Ostrava 2 Compulsory study plan
2014/2015 (N6202) Economic Policy and Administration (6202T010) Finance (01) Finance P Czech Ostrava 2 Compulsory study plan
2013/2014 (N6202) Economic Policy and Administration (6202T010) Finance (01) Finance P Czech Ostrava 2 Compulsory study plan

Occurrence in special blocks

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