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Lecture_2025.pdf | Lecture 8 | PhDr. Jaromír Baxa, Ph.D. |
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The course concentrates on the practical use of econometric methods, reviewing the relevant methodology, its use, and possible alternative modeling approaches. The lectures are supplemented by computer classes, where students can gain hands-on experience in applied econometric analysis. During the course, we will especially focus on time series techniques applied to forecasting asset volatility, modeling inflation, exchange rate volatility, and other topics that you may regularly encounter in economic and financial literature. The course focuses on the following topics in econometrics: OLS, IV, ARIMA, GARCH, VAR, cointegration, filters, and limited dependent variables.
Last update: Horváth Roman, prof., Ph.D. (10.02.2023)
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Students will learn the basics of time series econometrics with an emphasis on how to apply these methods. Last update: Horváth Roman, prof., Ph.D. (10.02.2023)
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G Grading - in line with the Dean's decree 17/2018. Exam at the end of the semester (80% weight) Term paper (20% weight) Exam dates: 19th May 2PM room 206, 2nd June 2PM room 206, 11th June 10AM room 206
Please register for the exam using the SIS. The details regarding the term paper are available in Lecture 1.pdf and in separate pdf files posted (during the semester) in the SIS. Last update: Horváth Roman, prof., Ph.D. (17.03.2025)
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Selected recommended textbooks on applied econometrics: Brook, Ch.: Introductory Econometrics for Finance, New York: Cambridge University Press.
Last update: Horváth Roman, prof., Ph.D. (10.02.2023)
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Lectures accompanied by seminars in computer room 016. The software R will be used during the seminars, and materials will be processed in the interactive Jupyter Notebook .ypinb format (freeware, available on all computers in 016). If you want to work on your computer, ensure in advance Jupyter is properly connected to the R kernel. If you are new to R and have not used Jupyter so far, study the Intro_to_jupyter+R.zip in Files and review the recorded introductory lectures on R basics, Data structures, Data input, and Basic data management from Data Analysis in R and/or the first recorded lectures from Data Science with R. Last update: Kukačka Jiří, PhDr., Ph.D. (14.02.2023)
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Grading - in line with the Dean's decree 17/2018. Exam at the end of the semester (80% weight) Term paper (20% weight) The details regarding the research proposal and term paper are available in Lecture 1.pdf and in separate pdf files posted (during the semester) in the SIS. Last update: Horváth Roman, prof., Ph.D. (28.02.2025)
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1. Introduction 2. OLS and basics 3. Introduction to Time Series 4. ARIMA Modeling 5. GARCH (2 lectures) 6. Introduction to Cointegration 7. Vector Autoregression 8. TSLS, IV 9. Non-linear time series models 10. Limited dependent variable models in finance 11. Time series filters 12. Networks 13. Guest lecture Last update: Horváth Roman, prof., Ph.D. (10.02.2023)
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