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Advanced time-series analysis

General data

Course ID:	2400-M1IiEZASC
Erasmus code / ISCED:	14.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0311) Economics The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Advanced time-series analysis
Name in Polish:	Zaawansowana analiza szeregów czasowych
Organizational unit:	Faculty of Economic Sciences
Course groups:	(in Polish) Przedmioty kierunkowe (obowiązkowe) do wyboru - studia II stopnia IE - grupa 2 (3*30h) (in Polish) Przedmioty obowiązkowe dla I r. studiów magisterskich drugiego stopnia - Informatyka i Ekonometria
ECTS credit allocation (and other scores):	4.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Type of course:	obligatory courses
Short description:	The course offers practial workshops with R and RMarkdown languages. Practical examples of modern time series analysis will be presented: Box-Jenkins procedure, ARIMA/SARIMA models, ECM/VAR/VECM models, univariate and multivariate models from GARCH family
Full description:	1. introduction to R 2. stationarity, random walk, stochastic trends, stationarity testing, spurious regressions, Newbold-Davies experiment. 3. AR and MA processes and their properties. 4. ARIMA and SARIMA models: estimation, diagnostics and forecasting 5. ECM, VAR and VECM models: long-term relationships among time series, error correction model 6. volatility modeling: univariate GARCH models, diagnostics, extensions of GARCH models, practical applications (estimationg Value-at-Risk, option pricing) 7. volatility modeling: multivariate GARCH models (EWMA, DVEC, BEKK, CCC, DCC) 8. creating dynamic documents with Rmarkdown language
Bibliography:	1. Tsay (2013) An Introduction to Analysis of Financial Data with R 2. Biecek (2016) Przewodnik po pakiecie R 3. Tsay (2010) Analysis of Financial Times Series, Wiley 4. Brooks (2014) Introductory Econometrics for Finance, CUP 5. Gągolewski (2014) Programowanie w języku R 6. Suchwałko, Zagdański (2019) Analiza i prognozowanie szeregów czasowych
Learning outcomes:	After the course student will know: • what is stationarity of time series, white noise, autocorrelation and partial autocorrelation functions, • how ARIMA/SARIMA models are constructed • how ECM, VAR and VECM models are constructed • how GARCH models are constructed will understand: • conception of time-series cointegration and their long-term relationship • conception of the error correction mechanism • conception of conditional variance and univariate and multivariate models from GARCH family. will be able to: • estimate models from ARIMA/SARIMA famili, do diagnostic analysis and produce forecasts • assess ex-post quality of the forecast • estimate models from ECM/VAR/VECM family and interprete their results • estimate models from GARCH family, produce forecasts of conditional variance and apply the model to particular problems • create dynamic dokument with RMarkdown language.
Assessment methods and assessment criteria:	Home taken project and class activity

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO TU W TH FR KON KON
Type of class:	Seminar, 30 hours more information
Coordinators:	Aneta Dzik-Walczak
Group instructors:	Aneta Dzik-Walczak
Students list:	(inaccessible to you)
Examination:	Course - Grading Seminar - Grading

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