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Time Series

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 3 модуль

Преподаватели

Course Syllabus

Abstract

The course is devoted to methods and models of time series analysis and forecasting. The specific results of this discipline are the familiarization of methods for adjusting data for inflation and seasonality, methods for reducing a time series to a stationary one, building AR, MA and ARMA models of a time series, building volatility models, building nonlinear models for analyzing one-dimensional time series, building VAR models, forecasting using these models, analyzing time series cointegration. The study of this discipline is based on the following disciplines: Probability Theory, Statistics, Econometrics. The main provisions of the discipline should be used in the future when studying the discipline of Risk Management, Financial Risk management, Quantitative methods in finance, as well as in the preparation of term paper and bachelor's thesis.
Learning Objectives

Learning Objectives

  • The aim of the course is to form the student's competencies for the analysis and forecasting of one-dimensional and multidimensional time series.
Expected Learning Outcomes

Expected Learning Outcomes

  • The student can count simple and logarithmic growth rates (profitability). The student is able to adjust the time series for inflation and seasonality. Able to measure the presence of autocorrelation in a time series (ACF, Leung-Box test). Able to conduct stationarity tests: ADF, KPSS, PP. Knows the basic models of data generation: examples. Knows the properties of AR, MA and ARMA models..
  • The stident is able to reduce the time series to a stationary series. Able to evaluate the parameters of the AR, MA, ARMA model.Able to test the ARCH effect in the residuals. Knows the basic commands of R Studio. The stident is able to build an interval and simulation forecast.
  • Able to evaluate the order and evaluate the parameters of ARCH and GARCH models. Able to use these models for forecasting. Knows other specifications of volatility models: ARCH, GARCH-M, EGARCH, TGARCH.
  • Able to check multiple time series for the presence of cointegration. Able to predict a pair of cointegrated time series.
  • Is able to conduct a test for the presence of structural gaps in the data. Able to evaluate the parameters of the mode switching model. Able to evaluate the parameters of threshold models and models with a trend component. Able to make a forecast based on a nonlinear model and evaluate the predictive accuracy of the model.
  • Is able to test time series for the presence of cause-and-effect relationships. Able to evaluate the order and parameters of the vector autoregression model. Able to conduct the Chow test (F-test) and LR test. Able to build an interval and simulation forecast using a VAR model. Able to evaluate the parameters of the VMA model and the VARMA model. Able to calculate pulse response functions (IRF).
Course Contents

Course Contents

  • Introduction to Time Series Analysis: Autocorrelation and Stationarity
  • Linear models of one-dimensional time series
  • Conditional volatility models
  • Nonlinear models of one-dimensional time series
  • Linear models of multidimensional time series
  • Cointegration
Assessment Elements

Assessment Elements

  • non-blocking Individual task
  • blocking Exam
    Oral examination (a survey based on course materials and an additional task). The student's response is recorded.
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.6 * Exam + 0.4 * Individual task
Bibliography

Bibliography

Recommended Core Bibliography

  • Klaus Neusser. (2016). Time Series Econometrics. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sptbec.978.3.319.32862.1
  • Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Vol. Second edition). Hoboken, New Jersey: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=985114

Recommended Additional Bibliography

  • Levendis, J. D. (2018). Time Series Econometrics : Learning Through Replication. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2016053
  • Palma, W. (2016). Time Series Analysis. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1229817
  • Подкорытова О. А., Соколов М. В. - АНАЛИЗ ВРЕМЕННЫХ РЯДОВ 2-е изд., пер. и доп. Учебное пособие для бакалавриата и магистратуры - М.:Издательство Юрайт - 2019 - 267с. - ISBN: 978-5-534-02556-9 - Текст электронный // ЭБС ЮРАЙТ - URL: https://urait.ru/book/analiz-vremennyh-ryadov-433180