Магистратура
2021/2022
Эконометрика (продвинутый уровень)
Статус:
Курс обязательный (Финансы)
Направление:
38.04.08. Финансы и кредит
Кто читает:
Банковский институт
Где читается:
Банковский институт
Когда читается:
1-й курс, 2 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
12
Охват аудитории:
для своего кампуса
Преподаватели:
Семерикова Елена Вячеславовна
Прогр. обучения:
Финансы
Язык:
английский
Кредиты:
5
Контактные часы:
12
Course Syllabus
Abstract
The course will be a core one for the Banking Institute Master program “Financial Analyst”. The course is intended for studying during the first and the second semester of the Master level education. The course is a prerequisite for some both core and specialized courses of the curriculum. Because of study of material of the course, a student should master and be able to prove the basic facts of strict development of classical econometrics. She/he should also know main ideas of univariate and multivariable time-series analysis including Box-Jenkins approach, ARIMA (p, d, q) models, non-stationary time-series, unit root tests, co-integration, VAR and VECM.
Learning Objectives
- The purpose of the course is to give students new and extended skills in both econometric tools and their application to contemporary economic problems. The main studying purpose of such topics is to clear understanding of econometric ideas, assumptions under which econometric approaches can be applied. The student should have skills of application of the indicated tools and methods to researches in problems of Micro-, Macroeconomics and Finance. The student should have knowledge and skills of “Econometrics” (Bachelor level) and a number of mathematical and statistical courses such that “Linear algebra”, “Statistics”, “Probability theory”.
Expected Learning Outcomes
- The student should have skills of application of the indicated tools and methods to researches in problems of Micro-, Macroeconomics and Finance.
Course Contents
- OLS
- Model Specification
- Multicollinearity, Heteroskedasticity and Autocorrelation
- Endogeneity and Instrumental Variables
- Project Assignment 1
- Maximum Likelihood and Models with Limited Dependent Variables
- Time-series econometrics. Univariate time series
- Time-series econometrics. Multivariate time series
- Panel Data Analysis
- Project Assignment 2
- Introduction. General concept of regression.
- The geometry of linear regression
- Classical linear regression (CLR)
- OLS under assumption of normality
- Multicollinearity
- Models with stochastic repressors
- Maximum likelihood (ML) estimators and OLS-estimators under normality assumption
- Linear regression with heterogeneous observations
- Linear regression under non-spherical disturbances. GLS
- Linear regression under serial correlation
- Linear regression diagnostics. Specification errors. Model selection
- Dynamic regression with lagged variables
- Time series econometrics
- Panel Data Models. Fixed effects. Random effects
- Two-stage least squares. Three-stage least squares
Interim Assessment
- 2021/2022 2nd module0.16 * Project Assignment 1 + 0.2 * Project Assignment 2 + 0.64 * Еженедельные тесты
Bibliography
Recommended Core Bibliography
- A guide to modern econometrics, Verbeek, M., 2008
- Jaksa Cvitanic, & Fernando Zapatero. (2004). Introduction to the Economics and Mathematics of Financial Markets. The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.mtp.titles.0262532654
- Verbeek, M. (2017). A Guide to Modern Econometrics (Vol. 5th edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639496
- Verbeek, M. (DE-588)170802655, (DE-576)164668535. (2012). A guide to modern econometrics / Marno Verbeek. Chichester: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.357323661
Recommended Additional Bibliography
- Applied econometric time series, Enders, W., 2004
- Mills, T. C., & Markellos, R. N. (2008). The Econometric Modelling of Financial Time Series: Vol. 3rd ed. Cambridge University Press.