Business Cycles Theory - JEM017

• Title: Business Cycles Theory
• Guaranteed by: Institute of Economic Studies (23-IES)
• Faculty: Faculty of Social Sciences
• Actual: from 2012 to 2013
• Semester: winter
• E-Credits: 6
• Examination process: winter s.:
• Hours per week, examination: winter s.:2/2, Ex [HT]
• Capacity: unlimited / unlimited (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Additional information: http://dl1.cuni.cz/course/view.php?id=880
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: prof. Ing. Miloslav Vošvrda, CSc.; PhDr. Jaromír Baxa, Ph.D.
• Teacher(s): PhDr. Jaromír Baxa, Ph.D.; Mgr. Lukáš Vácha, Ph.D.
• Class: Courses for incoming students

Annotation

Last update: PhDr. Jaromír Baxa, Ph.D. (09.09.2020)

In this course, students learn how to use the methods of current macroeconometrics. We start with isolation of trends and cycles, and with modelling univariate time series. More advanced topics, i.e., spectral analysis, filters, regime-shift models and state-space models, follow. The second part of the semester is devoted to multivariate models, forecasting, and identification of causal relationships in macroeconomics. We cover the recently developed approaches to identification such as external instruments in VAR or high-frequency identification as well.
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.

Course completion requirements

Last update: PhDr. Jaromír Baxa, Ph.D. (09.09.2020)

Problem sets and presentations 60%, Midterm 20%, Final exam 20%.
Problem sets: 10 points for each problem set at maximum. Late submission/returned PS -1 points. Presentation: 10 points (2-3 presentations per semester).

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.

Literature

Last update: 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

Syllabus

Last update: 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.