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Lecturer(s)
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Zapletal David, Mgr. Ph.D.
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Linda Bohdan, doc. RNDr. CSc.
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Course content
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Stochastic proces and its stationarity Autocorrelation and parcial autocorrelation function Stationarity models AR, MA, ARMA models (properties, identifying, goodness of fit) Nonstationary (integrated) models Random walk, ARIMA models, unit-root tests Seasonal models Forcasting by linear models Conditional heteroskedasticity models ARCH model, GARCH model, GARCH variants Forcasting using GARCH model
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with text (with textbook, with book), Methods of individual activities, Skills training
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Learning outcomes
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Students will be able to apply obtained skills in solving concrete mathematical, technical and economic problems.
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Prerequisites
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Supposes knowledge secondary mathematics to the extent of warps secondary school and inclusion from subject mathematical seminar.
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Assessment methods and criteria
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Written examination, Home assignment evaluation, Didactic test
Assignment: preparing and defending the project.
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Recommended literature
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ARLT, J., ARLTOVÁ, M. Ekonomické časové řady. Praha: GRADA Publishing, 2007.
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ARLT, J., ARLTOVÁ, M. Finanční časové řady. Praha: GRADA Publishing., 2003.
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ARLT, J. Moderní metody analýzy ekonomických časových řad. Praha: GRADA Publishing, 1999.
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CIPRA, T. Analýza časových řad. Praha: SNTL, 1986.
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MAKRIDAKIS, S., WHEEL WRIGHT, S. C., McGEE, V. R. Forecasting Methods and Applications. New York: John Wiley, 1983.
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RUBLÍKOVÁ, E., ARLT, J., ARLTOVÁ, M., LIBIČOVÁ, L. Analýha časových radov - Zbierka príkladov. Bratislava: EKONOM, 2003.
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