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# Time Series Analysis-1

2019/2020
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
1 year, 3 module

### Программа дисциплины

#### Аннотация

This course provides tools for quantitative analysis of economic time series, for example, macroeconomic and financial series. The course is an introduction to univariate statistical methods of analysis and forecasting of economic and financial time-series. ARIMA(p,d,q,) model, stationarity concept, Box-Jenkins approach and different forecasting issues are discussed in this course. Models and concepts from this course can be used in stochastic processes, mathematical models in economics, optimal control problems, statistical forecasting, financial mathematics, decision making under uncertainty. The stress in the course is made on the sense of facts and methods of time series analysis. Conclusions and proofs are given for some basic formulas and models; this enables the students to understand the principles of economic theory. The main stress is made on the economic interpretation and applications of considered economic models.

#### Цель освоения дисциплины

• providing tools for quantitative analysis of economic time series, for example, macroeconomic and financial series

#### Результаты освоения дисциплины

• get acquainted with the main concepts of time series theory and methods of analysis
• know how to use them in examining economic and financial processes
• understand methods, ideas, results and conclusions that can be met in the majority of books and articles on economics and finance
• should master traditional methods of time series analysis, intended mainly for working with time series data
• understand the differences between cross-sections and time series
• understand specific economic problems, which occur while working with data of these types
• get knowledge about how to analyze economic time series, what potential problems and pitfalls can exist in such cases
• get knowledge about how to compare different statistical models using in-sample and out-of-sample statistical measures of accuracy

#### Содержание учебной дисциплины

• Time series as a discrete stochastic process. Stationarity of stochastic processes.
• Autoregression-moving average model ARMA(p,q). Autocorrelation and partial autocorrelation functions.
• Estimation of coefficients in ARMA(p,q) processes. Information criteria.
• Forecasting using Box-Jenkins approach.
• Nonstationary time series. Box-Jenkins approach for determination of a degree of integration of time series.

#### Элементы контроля

• Home assignment (неблокирующий)
No late submissions of the home assignment are allowed.
• Final test (неблокирующий)
in the form of a written exam

#### Промежуточная аттестация

• Промежуточная аттестация (3 модуль)
0.7 * Final test + 0.3 * Home assignment

#### Рекомендуемая основная литература

• Applied econometric time series, Enders W., 2004
• Solutions manual to accompany Johnston/ DiNardo "Econometric methods", 4th ed., Kawakatsu H., 1997
• The econometric modelling of financial time series, Mills T. C., 2004
• Канторович Г.Г. (2002). Лекции: Анализ Временных Рядов. Higher School of Economics Economic Journal Экономический Журнал Высшей Школы Экономики, (1), 85. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.scn.025886.16537823