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 | Compulsory |

Year | 2 | Semester | winter |

Study language | English | ||

Year of introduction | 2012/2013 | Year of cancellation | 2022/2023 |

Intended for the faculties | EKF | Intended for study types | Follow-up Master |

Instruction secured by | |||
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Login | Name | Tuitor | Teacher giving lectures |

CHY0034 | Mgr. Ing. Lucie Chytilová, Ph.D. | ||

HAN60 | prof. Ing. Jana Hančlová, CSc. |

Extent of instruction for forms of study | ||
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Form of study | Way of compl. | Extent |

Full-time | Credit and Examination | 2+2 |

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.

Lectures

Individual consultations

Tutorials

Project work

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.

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.

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.

Preparation of the semester project and its defense.

LMS Moodle

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.

Subject has no prerequisities.

Subject has no co-requisities.

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 completion are defined only for particular subject version and form of study

Academic year | Programme | Branch/spec. | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2020/2021 | (N6202) Economic Policy and Administration | (6202T010) Finance | P | English | Ostrava | 2 | Compulsory | study plan | ||||

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 |

Block name | Academic year | Form of study | Study language | Year | W | S | Type of block | Block owner |
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2017/2018 Winter |