Course detail

Academic Writing

FP-awDAcad. year: 2020/2021

The course contents include identification and solution of econometric problems in the field of analysis of time series and cross-sectional data. Students will deepen their knowledge of the use of econometric methods in modelling, estimating, analysing and predicting economic phenomena, thus creating preconditions for sound economic decision-making (especially in the corporate sphere).
· Introduction to econometrics and data processing; non-technical introduction to regression; regression model with a single explanatory variable; model of multiple regression;
· Heteroscedasticity; autocorrelation of random components;
· The method of instrumental variables; models of qualitative and limited explained variables;
· Vector autoregressive models; other econometrics methods, models and tools;
· Overview of econometric model applications in enterprise economics.

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

HILL, R. C., GRIFFITHS, W. E., LIM, G. C. Principles of econometrics. 4th ed. Hoboken: John Wiley & Sons, 2012. xxvi, 758 p. ISBN 978-0470873724.
ENDERS, W. Applied econometric time series. Fourth edition. Hoboken, NJ: Wiley, 2015. ISBN 978-111-8808-566.
KOOP, G. Introduction to econometrics. Chichester: John Wiley & Sons, 2008. 371 s. ISBN 978-0-470-03270-1.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Students will be evaluated on the basis of an individual written term paper (50 points); their theoretical knowledge will be tested in the discussion within an oral exam (50 points). Students must achieve 70 points for successful completion of the course.

Language of instruction

English

Work placements

Not applicable.

Course curriculum

The course contents include identification and solution of econometric problems in the field of analysis of time series and cross-sectional data. Students will deepen their knowledge of the use of econometric methods in modelling, estimating, analysing and predicting economic phenomena, thus creating preconditions for sound economic decision-making (especially in the corporate sphere).
Course content:
· Introduction to econometrics and data processing; non-technical introduction to regression; regression model with a single explanatory variable; model of multiple regression;
· Heteroscedasticity; autocorrelation of random components;
· The method of instrumental variables; models of qualitative and limited explained variables;
· Vector autoregressive models; other econometrics methods, models and tools;
· Overview of econometric model applications in enterprise economics.

The course objective is to develop and deepen knowledge, methods and skills in the area of exact means of description and examination of economic dependencies in connection with theoretical approaches of econometric methods that the students will use in their scientific work.

Aims

The course objective is to develop and deepen knowledge, methods and skills in the area of exact means of description and examination of economic dependencies in connection with theoretical approaches of econometric methods that the students will use in their scientific work.

Classification of course in study plans

  • Programme DSP-ŘEP-KS Doctoral, 2. year of study, winter semester, 0 credits, compulsory
    , 2. year of study, summer semester, 0 credits, compulsory
  • Programme DSP-CME Doctoral, 2. year of study, winter semester, 0 credits, compulsory
  • Programme DSP-ŘEP Doctoral, 2. year of study, summer semester, 0 credits, compulsory
  • Programme DSP-CME-KS Doctoral, 2. year of study, summer semester, 0 credits, compulsory
    , 2. year of study, winter semester, 0 credits, compulsory
  • Programme DSP-ŘEP Doctoral, 2. year of study, winter semester, 0 credits, compulsory
  • Programme DSP-CME Doctoral, 2. year of study, summer semester, 0 credits, compulsory

Type of course unit

 

Lecture

20 hours, optionally

Teacher / Lecturer