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.
Supervisor
Department
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