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Бакалавриат 2019/2020

## Анализ временных рядов

Статус: Курс по выбору (Экономика)
Направление: 38.03.01. Экономика
Когда читается: 4-й курс, 1 модуль
Формат изучения: Blended
Язык: английский
Кредиты: 4

### Course Syllabus

#### Abstract

The course aims to provide students with a theoretical understanding of the basics of time series modeling and demonstrate their application on real data. This course is blended. Students learn theory from the online course “Macroeconometric forecasting” on the EDX platform, developed by the International monetary fund. On practical session, they will apply models to macroeconomic and financial data. The course begins with essentials of working with time series data. The next part of the course covers all basic time series models, such as: ARIMA, SARIMA, ARCH and GARCH, VAR and VECM. As a result of the course, student will make a project on real data: prepare data for analysis, choose appropriate model, apply it and interpret results. The practical session R language is applied, some basics of it are trained through DataCamp cources. #### Learning Objectives

• Analyze economic data in accordance with the task, make preliminary data analysis.
• Build appropriate econometric time series models for the research question, analyze and interpret results.
• Understand limitation and relevance of the models. #### Expected Learning Outcomes

• Know basic concepts of univariate time series analysis, build appropriate econometric time series models.
• Know basic concepts of multivariate time series analysis, build appropriate econometric time series models. #### Course Contents

• Univariate time series analysis
1. Stationary Time Series. Dealing with time series data. Concept of stationary and covariate-stationary series, autocorrelation and partial-autocorrelation functions, white Noise, autoregression models (AR), moving average (MA) models, ARMA models: properties, specification, estimation and forecasting. the Box–Jenkins methodology, diagnostic testing for model adequacy. Topic 2. Nonstationary Time Series. Problems arise due to nonstationary, unit roots and characteristic relations, testing nonstationary: the Dickey–Fuller and augmented Dickey–Fuller tests, KPSS test and Philip-Pearson test; series transformation: differencing, selecting order of difference and ARIMA model. Dealing with seasonal data: series decomposition into stationary and trend and(or) seasonal component, Fourier decomposition and periodogram, SARIMA models. ARCH-GARCH model to deal with nonstationarity due to non-constant dispersion. Forecasting and forecasting errors.
• Mutivariate time series analysis
3. Vector Autoregression (VAR). Reduced and structural VAR Forms, model estimation, model conditions, vector AR(p) models, vector moving average models, lag specific criteria: LM test, Granger causality test, exogeneity in a VAR, the impulse-response function, forecasting with VAR: dynamic, static, stochastic and deterministic solutions. 4. Structural Vector Autoregression (SVAR). SVAR specification, comparison with reduced form VAR, structural impulse responses, Choleski decomposition, Blanchard-Quah decomposition, variance decomposition, identification strategies: recursive and non-recursive. 5. Vector Error Correction Model (VECM). The concept of cointegration and LR relations, the Engle–Granger cointegration test, the Johansen full-information maximum likelihood cointegration test, VECM specifications and estimation, lag length and causality tests, forecasting with VECM. #### Assessment Elements

• Test 1
• Test 2
• Self-study work (DC)
• Reports
• Exam #### Interim Assessment

• Interim assessment (1 module)
0.3 * Exam + 0.4 * Reports + 0.1 * Self-study work (DC) + 0.1 * Test 1 + 0.1 * Test 2 