Бакалавриат
2024/2025![Learning Objectives](/f/src/global/i/edu/objectives.svg)
![Expected Learning Outcomes](/f/src/global/i/edu/results.svg)
![Course Contents](/f/src/global/i/edu/sections.svg)
![Interim Assessment](/f/src/global/i/edu/intermediate_certification.svg)
![Bibliography](/f/src/global/i/edu/library.svg)
Эконометрика временных рядов
Статус:
Курс по выбору (Совместная программа по экономике НИУ ВШЭ и РЭШ)
Направление:
38.03.01. Экономика
Кто читает:
Школа финансов
Где читается:
Факультет экономических наук
Когда читается:
4-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Карамышева Мадина Ринатовна
Язык:
английский
Кредиты:
6
Контактные часы:
58
Course Syllabus
Abstract
We first review the basics of time series econometrics. Then, in more details, we look at the VAR class of models, including VAR, VARX, VECM, GVAR, and its rather broad application to macroeconomics, including fiscal and monetary policy and some finance applications. After that, we cover ARCH, GARCH with its application to value at risk and contagion. Course Prerequesites: Linear Algebra, Probability Theory, Mathematical Analysis, Basic Econometrics.
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