BPE_CARA Time Series

Faculty of Economics and Administration
Spring 2017
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
prof. Ing. Osvald Vašíček, CSc. (lecturer)
Ing. Mgr. Jakub Buček (seminar tutor)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable
Tue 11:05–12:45 P106
  • Timetable of Seminar Groups:
BPE_CARA/T01: Thu 23. 2. to Mon 22. 5. Thu 13:00–14:35 115, J. Buček, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPE_CARA/01: Tue 12:50–14:30 VT204, D. Němec
BPE_CARA/02: Tue 14:35–16:15 VT204, D. Němec
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 21 fields of study the course is directly associated with, display
Course objectives
The course is devoted to mathematical-statistical approaches to the analysis of economic processes described by time series. The introductory part of the course is focused on the decomposition approach to the time series analysis. The second part of the course deals with the Box-Jenkins methodology of the time series analysis. The students will learn the procedures of identification of a suitable model of the time series and the criteria for the suitable model verification. The last section of the course will be devoted to business cycle analysis. Business cycles will be analyzed with help of selected filtration methods.
All studied areas will place emphasis on the students' ability to use the gained knowledge in practice.
The main objective of the course is to provide the students with knowledge and skills, which are necessary for practical utilization of the time series analysis. At the end of the course the students should be able to analyze real data, create a suitable model for the data, construct future forecasts, evaluate and interpret gained outcomes and understand information about time series.
Syllabus
  • 1.Decomposition approach to the time series analysis: time series and its components: trend, eventual seasonality or cycles, and stochastic component. Trend models based on the modifications of the linear regression model: recognition and estimation of its parameters. Special methods designed for non-linearized trend forms.
  • 2.Moving averages and their exploitations in the process of the determination of the trend and/or seasonality. Their building at the local adjustment with the polynomial curves. Exponential smoothing(Brown),Holt's and Winters' adjustment method.
  • 3.Modelling of the one-dimensioned time series: autocorrelation properties of the time series, the basic models based on the Box-Jenkins methodology(AR,MA a ARMA modely),identification and diagnostics of the model(choice of rank od the model, tests of stability). ARIMA-models and some of their generalized forms.
  • 4.Forms of the possible non-stationarity of the time series, and approaches leading to the stationary state of the series. Random walk model. Unit-root tests (Dickey-Fuller´s test and others) indicating the non-stationary character of the time series. Autoregressive model of the distributed lags.
  • 5.Modelling of the volatility. Autoregressive models with conditioned heteroskedasticity: ARCH models,GARCH models and modifications. Models non-linear in the mean. Applications focusing on the financial time-series analysis.
  • 6.Modelling of the multi-dimensional time series: the principles and estimation methods. Vector autoregression. Testing of dependencies among variables: Granger non-causality. Impuls response. Cointegration among several time series, ECM(error correction models).
Literature
    required literature
  • Arlt, Josef; Arltová, Markéta: Ekonomické časové řady. Professional Publishing 2009. ISBN 978-80-86946-85-6.
  • CIPRA, Tomáš. Finanční ekonometrie. 1. vyd. Praha: Ekopress, 2008, 538 s. ISBN 9788086929439. info
    recommended literature
  • ENDERS, Walter. Applied econometric time series. 2nd ed. Hoboken: John Wiley & Sons, 2004, xiv, 460. ISBN 0471230650. info
Teaching methods
lectures, computer labs practices, class discussion, homework, individual project
Assessment methods
The course consists of lectures and seminars. The course is concluded by the oral exam. Students can attend the exam if they fulfill these conditions: active attendance at the seminars, passing two tests during the semester and successful solution of the semestral project.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2017, recent)
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