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Lecturer(s)
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Fišerová Eva, doc. RNDr. Ph.D.
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Course content
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1. Multiple regression models: estimation, inference and prediction 2. Multiple regression models: multicolinearity, heteroskedasticity 3. Regression with qualitative variables: dummy variables techniques 4. Regression with random predictors: instrumental variables techniques 5. Panel data methods: pooled model, fixed and random effects models 6. Advanced regression models: simultaneous equations models 7. Time series models: stationarity, serial correlation, heteroskedasticity 8. Advanced time series models: nonstationarity, cointegration 9. Advanced time series models: ARCH and GARCH models
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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Understanding statistical procedures appropriate for modelling and statistical analysis of economic data
Understanding Understatnding statistical procedures appropriate for modelling and statistical analysis of economic data
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Prerequisites
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Basic knowledge of probability theory and mathematical statistics.
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Assessment methods and criteria
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Oral exam
Credit: active participation in exercises; the student independently solves assigned tasks, in which demonstrates an understanding of statistical methods suitable for economic data and the ability to actively work with them. Exam: the student demonstrates knowledge and understanding of statistical methods suitable for statistical analysis of economic data.
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Recommended literature
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A.H. Studenmund. (2017). Using Econometrics: A Practical Guide (7th edition).. Pearson International.
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C. Colonescu. (2016). Principles of Econometrics with R.
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C. Hill, W. Griffiths, G. Lim. (2011). Principles of econometrics (4th edition). Wiley.
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J.M. Wooldridge. (2015). Introductory Econometrics: A Modern Approach (6th edition).. Cengage Learning, Boston.
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