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Школа финансов ВШЭ
119049 , Покровский бульвар, 11,
офис S629.
Телефоны:
+7 (495) 772-95-90*27447, *27190, *27947 (по общим вопросам Школы финансов)
+7 (495) 621-91-92 (по вопросам Бизнес-образования)
+7 (495) 916-88-08 (Магистерская программа "Корпоративные финансы")
E-mail:
df@hse.ru (по общим вопросам Школы финансов),
finance@hse.ru (по вопросам Бизнес-образования)
ординарный профессор НИУ ВШЭ, доктор экономических наук, заслуженный работник высшей школы РФ
Школа финансов ВШЭ — лидирующий в стране центр компетенций в области корпоративных финансов, оценки стоимости, банковского дела, фондового рынка, управления рисками и страхования, учета и аудита.
Наш университет - первый в России в глобальном рейтинге "QS – World University Rankings by subject" (2022) в предметной области Accounting and Finance, а так же первый среди российских университетов в области Business & Management Studies.
Российские корпорации на пути к антихрупкости. Финансовая архитектура компаний
Григорьева С. А., Жукова Ю. Д., Завертяева М. А. и др.
М.: Издательский дом НИУ ВШЭ, 2025.
Grishunin S., Ivashkovskaya I., Brendeleva N. et al.
Journal of Corporate Finance Research. 2025. Vol. 19. No. 1. P. 25-40.
Badr I., Rawnaa Ibrahim, Hussainey K.
In bk.: Opportunities and Risks in AI for Business Development. Vol. 2: Opportunities and Risks in AI for Business Development. Prt. 636. Springer Cham, 2025. P. 385-399.
Financial Economics. WP HSE. HSE University, 2025. No. 1/FE/2025.
Times Series Econometrics
Преподаватели
Course Syllabus
Abstract
Learning Objectives
- The objective of this course is to provide the student with tools for empirical analysis of time series and to show how econometric models can be applied to empirical models in macroeconomics and finance.
Expected Learning Outcomes
- Apply econometric models to empirical models in macroeconomics and finance
- Understand the difference between various univariate models, be able to analyze the process and choose an appropriate model.
- Detect unit roots, understand the difference between stationarity and non-stationarity
- being able to condust forecasting with ARIMA models
- Being able to apply various identification schemes to SVAR models, being able to understand how VAR are estimate, how to construct IRF and FEVD
- Being able to apply obtained knowledge to real-life finance, economics problems
- Being able to estimate variaous conditional volatility models and apply them to real-life finance problems
Course Contents
- Introduction/reviewing of time series econometrics
- Non-stationarity
- ARIMA
- Multivariate Time Series Models. VAR
- VAR applications
- Modeling the conditional variance (ARCH, GARCH, Multivariate GARCH)
Interim Assessment
- 2024/2025 2nd module0.25 * Big practical group project + 0.25 * Final test + 0.2 * Home assignments + 0.25 * Midterm test + 0.025 * Quizzes + 0.025 * Quizzes
Bibliography
Recommended Core Bibliography
- Applied econometric time series, Enders, W., 2004
- James Douglas Hamilton. (2020). Time Series Analysis. Princeton University Press.
Recommended Additional Bibliography
- Hamilton, J. D. . (DE-588)122825950, (DE-576)271889950. (1994). Time series analysis / James D. Hamilton. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.038453134