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Soubory | Komentář | Kdo přidal | |
JEM017_DetailedSyllabus_2020.pdf | Syllabus | PhDr. Jaromír Baxa, Ph.D. |
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Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (09.09.2020)
Over the semester, students are expected to apply the methods in regular problem sets and to present their results in the seminars. Problem sets shall be written in R and delivered as Jupyter notebooks. Sample R-codes are provided. |
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Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (09.09.2020)
Problem sets and presentations 60%, Midterm 20%, Final exam 20%. About 10 problem sets shall be expected. It is necessary to have at least 50% of points of each problem set to pass the course. Midterm: written exam. Final exam: presentation of selected problem set and written exam. |
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Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (09.09.2020)
Literature We provide most of the necessary information in our presentations and in sample codes. If needed, we encourage students to consult in the following textbooks and in articles mentioned in the syllabus. Enders, W.: Applied Econometric Time Series, 3rd ed., Wiley, 2009. Kilian, L., & Lütkepohl, H.: Structural Vector Autoregressive Analysis. Cambridge: Cambridge University Press, 2017. Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer, 2005. Kočenda, E., Černý, A.: Elements of Time Series Econometrics: An Applied Approach, Karolinum 2007. Ramey, V. A. (2016). Macroeconomic shocks and their propagation. In Handbook of Macroeconomics (Vol. 2, pp. 71-162). Elsevier.
Moodle Site: http://dl1.cuni.cz/course/view.php?id=880 |
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Poslední úprava: PhDr. Jaromír Baxa, Ph.D. (09.09.2020)
1. Introduction to the course. Study requirements. 2. Stationary linear models. AR, MA, ARMA models and their properties. Stationarity: economic and econometric interpretation, unit-root tests. 3. Nonstationary models, unit-root tests under structural instability. 4. Introduction to spectral analysis. 5. Filters and identification of business cycles. 6. Kalman filter and state-space models. 7. Classical business cycles analysis: turning points, non-linear models and leading indicators. 8. VAR models: Estimation and forecasting. 9. Identification in VAR models. Recursive identification, structural VARs, sign-restrictions and narrative approach. 10. VARs with non-stationary variables. Cointegration. 11. Bayesian VARs and Big data in Macroeconometrics. 12. Recent approaches to identification. Local projections, external instruments and the proxy SVAR model, high-frequency identification. |