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Бакалавриат 2022/2023

Анализ временных рядов

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус: Курс по выбору (Экономика)
Направление: 38.03.01. Экономика
Где читается: Факультет менеджмента (Пермь)
Когда читается: 4-й курс, 1, 2 модуль
Формат изучения: с онлайн-курсом
Онлайн-часы: 10
Охват аудитории: для всех кампусов НИУ ВШЭ
Язык: английский
Кредиты: 5
Контактные часы: 42

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

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

Expected Learning Outcomes

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

Course Contents

  • Univariate time series analysis
  • Mutivariate time series analysis
Assessment Elements

Assessment Elements

  • non-blocking Test
  • non-blocking Exam
  • non-blocking Reports
  • non-blocking DC (DataCamp)
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.25 * Test + 0.05 * DC (DataCamp) + 0.15 * Reports + 0.4 * Exam
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
  • 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

Recommended Additional Bibliography

  • Bleikh, H. Y., & Young, W. (2013). Time Series Analysis and Adjustment : Measuring, Modelling and Forecasting for Business and Economics. Farnham: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=531761
  • 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

Authors

  • Shenkman Evgeniia Andreevna
  • Борисова Елена Феликсовна