Stat. Modelling and Time Series Analysis

Zkratka předmětu KMI/YSMAC
Název předmětu Stat. Modelling and Time Series Analysis
Akademický rok 2019/2020
Pracoviště / Zkratka KMI/YSMAC
Název Statistical Modelling and Time Series Analysis
Akreditováno/Kredity Ano/6
Rozsah hodin Přednáška 3 HOD/TYD Cvičení 1 HOD/TYD
Vyučovací jazyk angličtina
Nahrazovaný předmět KMI/SMAC
Vyloučené předměty
Podmiňující
Způsob zakončení Zkouška
Forma zakončení Kombinovaná
Zápočet před zkouškou Ano
Vyučovaný semestr Letní
Cíle předmětu (anotace)

The aim of the course is to introduce regression analysis and classical statistical methods for times series analysis - trend, seasonal and cycle adjustment and in short to introduce modern methods of time series analysis - Box-Jenkins methodology.

Požadavky na studenta

Credit Requirements:
To duly submit assignment tasks and to obtain at least 40% of points from credit tests. (Two tests during a semester).

Examination Requirements:
Exam has two parts - written and oral. In the written part, students have to prove that they can recognise types of optimization problems, to choose suitable methods to solve them and suggest a suitable solution. To pass this part it is necessary to obtain at least 50 percent of points from the test. The written part could be forgiven if the student has at least 65 percent of points from the credit tests. The oral examination is focused on work with PC and discussions about the solution of the written part of the examination. Final mark is based on the results of the credit tests, the written and oral parts of the examination. To pass the oral part it is necessary to answer at least one of the three given questions.

Obsah

Lectures:
1 - Introduction to the course, economical times series and its basic properties, assignments;
2 - Correlation - correlation arrays, correlation coeficients.
3 - Introduction to linear regression, least square method.
4 - Linear regression, different types of dependence, curve fitting.
5 - Linear regression - practical applications, coefficient of determination, normal model, assumptions and applications.
6 - Introduction to Time series models, objectives of TS analysis, errors ,measures of goodness of fit in times series.
7 - classical model of economical times series (trend, seasonality, long-term cycle), decomposition of time series, trend tests.
8 - Seasonality in TS - Small trend method, regression methods.
9 - Periodicity in TS - spectral analysis, periodogram, tests.
10 - Adaptive modelling in TS, using of moving averages.
11 - Exponencial smoothing in times series context.
12 - Randomness tests. Autocorrelation, stationarity.
13 - AR and MA models.
14 - Introduction into Box-Jenkins metodology.

Předpoklady - další informace k podmíněnosti studia předmětu

Equivalence: Statistické modelování a analýza časových řad - SMAC, KSMAC

Získané způsobilosti

Students understand the basic principles of regression analysis and times series analysis and are able to apply these method to solution economical problems. Students are able to use software to carry out appropriate analysis.

Garanti a vyučující
  • Garanti: doc. RNDr. Jana Klicnarová, Ph.D.
  • Přednášející: Mgr. Michal Houda, Ph.D., doc. RNDr. Jana Klicnarová, Ph.D.
  • Cvičící: Mgr. Michal Houda, Ph.D., doc. RNDr. Jana Klicnarová, Ph.D.
Literatura
  • Wooldridge, J.M. Introductory Econometrics: A Modern Approach. South-Western College pub, 2009.
  • DRAPER, N., SMITH, H.:. Applied Regression analysis. Wiley and Sons, New York, 1981.
  • MONTGOMERY, Douglas C, Cheryl L JENNINGS a Murat KULAHCI. Introduction to time series analysis and forecasting. Wiley-Interscience, c2008, xi, 445 s., 2008. ISBN 04-716-5397-7.
  • HAMILTON, James D. Time series analysis. Princeton: Princeton University Press, xiv, 799 s., 1994. ISBN 06-910-4289-6.
Vyučovací metody

Monologická (výklad, přednáška, instruktáž), Dialogická (diskuze, rozhovor, brainstorming), Práce s multimediálními zdroji (texty, internet, IT technologie), Blended learning

Hodnotící metody

Kombinovaná zkouška, Test

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